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SLAM for Visually Impaired Navigation: A Survey

December 2022

December 2022

Authors:

Banafshe Marziyeh Bamdad

University of Zurich

Alireza Darvishy

Zurich University of Applied Sciences

Preprints and early-stage research may not have been peer reviewed yet.

In recent decades, several assistive technologies have been developed to improve the ability of blind and visually impaired individuals to navigate independently and safely. At the same time, simultaneous localization and mapping (SLAM) techniques have become sufficiently robust and efficient to be adopted in developing these assistive technologies. We present the first systematic literature review of 54 recent studies on SLAM-based solutions for blind and visually impaired people, focusing on literature published from 2017 onward. This review explores various localization and mapping techniques employed in this context. We discuss the advantages and limitations of these techniques for blind and visually impaired navigation. Moreover, we examine the major challenges described across studies. We explain how SLAM technology offers the potential to improve the ability of visually impaired individuals to navigate effectively. Finally, we present future opportunities and challenges in this domain.

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SLAM for Visually Impaired People: a Survey

Marziyeh Bamdad1,2, Davide Scaramuzza1, and Alireza Darvishy2

1Department of Informatics, University of Zurich, 8050 Zurich, Switzerland

2Institute of Applied Information Technology, Zurich University of Applied Sciences, 88400 Winterthur, Switzerland

Corresponding author: Marziyeh Bamdad (e-mail: bamdad@iﬁ.uzh.ch).

ABSTRACT In recent decades, several assistive technologies have been developed to improve the ability of

blind and visually impaired individuals to navigate independently and safely. At the same time, simultaneous

localization and mapping (SLAM) techniques have become sufﬁciently robust and efﬁcient to be adopted in

developing these assistive technologies. We present the ﬁrst systematic literature review of 54 recent studies

on SLAM-based solutions for blind and visually impaired people, focusing on literature published from

2017 onward. This review explores various localization and mapping techniques employed in this context.

We systematically identiﬁed and categorized diverse SLAM approaches and analyzed their localization and

mapping techniques, sensor types, computing resources, and machine-learning methods. We discuss the

advantages and limitations of these techniques for blind and visually impaired navigation. Moreover, we

examine the major challenges described across studies, including practical considerations that affect usability

and adoption. Our analysis also evaluates the effectiveness of these SLAM-based solutions in real-world

scenarios and user satisfaction, providing insights into their practical impact on BVI mobility. The insights

derived from this review identify critical gaps and opportunities for future research activities, particularly

in addressing the challenges presented by dynamic and complex environments. We explain how SLAM

technology offers the potential to improve the ability of visually impaired individuals to navigate effectively.

Finally, we present future opportunities and challenges in this domain.

INDEX TERMS Navigation, SLAM, systematic literature review, visually impaired

I. INTRODUCTION

IN recent decades, there has been increasing research inter-

est in developing assistive technologies to enhance spatial

navigation for blind and visually impaired (BVI) individuals.

In most cases, the main goal is to guide and assist BVI people

in navigating safely in unknown environments without the

help of a sighted assistant. Navigation is a complex task; it

requires ﬁnding an optimal path to the desired destination,

perceiving the surroundings, and avoiding obstacles. Cru-

cially, all of these functionalities need to accurately localize

the BVI user in the environment. There are several approaches

for localization, such as the global positioning system (GPS),

radio frequency identiﬁcation (RFID), and simultaneous lo-

calization and mapping (SLAM) [1], [2]. Each has advantages

and challenges and is used in different applications.

GPS is a localization technique employed in outdoor sce-

narios owing to its affordability to the end user, wide coverage

of the Earth, and ease of integration with other technolo-

gies. However, this technique suffers from limitations like

satellite signal blockage, inaccuracy, and signal loss caused

by weather conditions, walls, and other obstacles [2]. Ap-

proaches based on RFID utilize small, low-cost tags for lo-

calization. To localize an agent, a set of RFID tags must be

installed in the environment [1]. Although localization can be

accurately performed using an RFID scheme, taking advan-

tage of this technique requires a pre-installed infrastructure.

A SLAM approach can offer a reliable alternative to RFID

and GPS. SLAM is an innovative technique that involves

simultaneously constructing an environment model (map)

and estimating the state of an agent moving within it [3]. The

SLAM architecture (Figure 1) consists of two fundamental

components: the front-end and the back-end. The front-end

receives environmental information from the sensors, ab-

stracts it into amenable models for estimation, and sends it to

the back-end [3]. The back-end is responsible for optimizing

the mapping, localization, and data fusion processes, which

collectively contribute to the accuracy and reliability of the

SLAM systems.

SLAM uses diverse types of sensors to determine an

agent’s position, location, and velocity and detect and avoid

obstacles, even in a dynamically changing unknown envi-

ronment. This technique uses infrared (IR) sensors, acoustic

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Bamdad et al.: SLAM for Visually Impaired People: a Survey

FIGURE 1. Front- and back-end in a typical SLAM system [3]

sensors, RGB cameras, inertial measurement units (IMUs),

ultrawide-band (UWB), LiDAR, RADAR, and RGB-D sen-

sors [4].

The collaborative effort between the front- and back-ends

empowers SLAM to provide a robust and real-time spatial

understanding, making it a valuable tool for various applica-

tions. The SLAM community has made tremendous strides

over the past 35 years, developing large-scale practical ap-

plications and seeing a steady transition of this technology

into the industry [3]. Cadena et al. [3] divided the life cycle

of SLAM into three periods: classical age (1986-2004), algo-

rithmic analysis age (2004–2015), and robust-perception age

(2015-present). The evolution of portable computation and

the availability of low-cost, highly accurate, and lightweight

sensors such as cameras and IMUs make them appropriate for

pedestrian navigation. By exploiting these advances, many

researchers have recently adopted SLAM to develop assis-

tive technology demonstrators to help BVI people navigate

unknown environments.

Since the ﬁrst electronic travel aids (ETAs) emerged ap-

proximately 70 years ago, the development of navigation

devices to guide BVI people through indoor and/or outdoor

environments has remained a challenge and a key concern

for researchers [5]. From traditional to deep-learning-based

navigation approaches, researchers have always faced chal-

lenges ranging from technical issues to the limitations of user

capabilities. As BVI navigation approaches must improve

real-time performance while reducing the size, weight, energy

cost, and overall price of the assistive system, these studies

have put a lot of effort into coping with constraints in com-

putational issues, sensory equipment, and portable devices.

They also need to provide solutions to calculate the precise

position and orientation of the user in a real-time manner.

However, the challenges of different scenarios, including

complex and cluttered environments, noisy environments,

and large spaces, must be considered.

Furthermore, efﬁcient and reliable obstacle detection in

both indoor and outdoor environments has always been a

concern. In this regard, other challenges include identifying

static and dynamic obstacles, predicting the risk of collision,

understanding moving objects’ motion and estimating their

speed, detecting small objects, and identifying obstacles at

different levels of the user’s body, from drops in terrain

to head level. In addition, an intuitive, user-friendly, low-

cognitive-load method to provide accurate and sufﬁcient en-

vironmental information to the user is also considered an

important research target. These methods should be improved

to provide adjustable and customized feedback on demand for

different users.

Moreover, assistive technology should provide user safety

and independence, hands-free operations, decreased effort,

and backup in the case of system failure. In addition to

the aforementioned challenges, deep-learning-based solu-

tions also have special issues, such as designing lightweight

neural network architectures to reduce computational expense

and provide sufﬁcient data for the training and validation of

the models.

This SLR is designed to act as a resource for the aca-

demic and research communities. The objective of this re-

view is to explore and highlight the strengths and potential

limitations of the current SLAM applications for visually

impaired navigation. This study aims to inform and guide

subsequent research. The insights derived from this review

identify critical gaps and opportunities for future research,

particularly for tackling the challenges presented by dynamic

and complex environments. Such environments pose unique

difﬁculties for visually impaired navigation, and addressing

them through advanced SLAM technologies could lead to sig-

niﬁcant improvements in both the effectiveness and reliability

of assistive solutions.

A. RELATED WORK

Thus far, many reviews have been conducted on assistive

technologies developed for BVI navigation. Several studies

reviewed walking assistance systems [5], [7]–[14] provided a

detailed classiﬁcation of the developed approaches. [12] cat-

egorized walking assistants into three groups: sensor-based,

computer vision-based, and smartphone-based. The authors

explained the technologies used and inspected each approach,

and evaluated some important parameters of each approach,

such as the type of capturing device, type of feedback, work-

ing area, cost, and weight. The work by [14] introduced tech-

niques and technologies designed to assist visually impaired

individuals in their mobility and daily lives. This comprehen-

sive review analyzes multiple mobility-assistive technologies

that are suitable for indoor and outdoor environments. It of-

fers insights into the various feedback methods employed by

assistive tools based on recent technologies. In addition, [7]

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Bamdad et al.: SLAM for Visually Impaired People: a Survey

reviewed wayﬁnding devices used by visually impaired indi-

viduals in real-world scenarios. This review aimed to provide

a comprehensive exploration of the various aids employed for

navigation while assessing their perceived efﬁcacy.

Some studies focused on indoor navigation for BVI

users [15]–[22] and some focused on computer vision-

based navigation systems [15], [23]–[27]. Among these stud-

ies, [15] conducted a systematic literature review of state-

of-the-art computer vision-based methods used for indoor

navigation. The authors described the advantages and limi-

tations of each solution under review, and included a brief de-

scription of each method. Furthermore, [21] comprehensively

examined existing methods and systems developed within the

domain of assistive technology, with a speciﬁc focus on ad-

dressing the unique needs and challenges faced by the visually

impaired. This study places strong emphasis on evaluating

methods that have practical applications in enhancing the

lives of visually impaired individuals.

Several review papers on wearable navigation systems

have also been published [28]–[33]. [28] have conducted

a systematic review with the primary objectives of analyz-

ing wearable obstacle avoidance electronic travel aids. Their

work delves into the strengths and weaknesses of existing

ETAs, providing a thorough evaluation of hardware func-

tionality, cost-effectiveness, and the overall user experience.

[29] provided a comprehensive understanding of wearable

travel aids by focusing on their designs and usability. Their

objectives included surveying the current landscape of travel

aid design, investigating key design issues, and identifying

limitations and future research directions. Furthermore, [30]

conducted a systematic review of the literature on wearable

technologies designed to enhance the orientation and mobil-

ity of the visually impaired. This review provides valuable

insights into the technological characteristics of wearables,

identiﬁes feedback interfaces, emphasizes the importance of

involving visually impaired individuals in prototype evalua-

tions, and highlights the critical need for safety evaluations. A

review by [31] provides a comprehensive review of computer

vision and machine-learning-based assistive methods. Exist-

ing ETAs are divided into two groups: active systems provid-

ing subject localization and object identiﬁcation, and passive

systems providing information about the users’ surroundings

using a stereo camera, monocular camera, or RGB-D camera.

Focusing on guide robots, [34] reviewed the multifaceted

objectives. Their work included a comparative analysis of

the existing robotic mobility aids and state-of-the-art tech-

nologies. This review highlights the potential of guide robots

to enhance the mobility and independence of the visually

impaired.

[35] and [36] reviewed studies with the focus on object

detection and recognition. [36] performed a review on object

recognition tailored to the needs of visually impaired individ-

uals. This review examines state-of-the-art object detection

and recognition techniques, focuses on standard datasets, and

emphasizes on the latest advancements. [35] reviewed studies

speciﬁc for staircase detection systems, primarily designed to

facilitate the navigation of visually impaired individuals. The

goal of this review is to provide a comprehensive comparative

analysis of these systems considering their suitability and

effectiveness.

Other similar studies include a survey of inertial mea-

surement units (IMUs) in assistive technologies for visually

impaired people [37], a review of urban navigation for BVI

people [38], a survey paper that reviewed assistive tools based

on white canes [39], and review papers exploring smartphone-

based navigation devices [40]–[42]. [40] reviewed the multi-

faceted objectives in the domain of smartphone-based nav-

igation devices. They aimed to provide a comprehensive

overview of smartphone use among people with vision im-

pairment, identify research gaps for future exploration, and

delve into the use of smartphones by individuals with vision

impairment and the accessibility challenges they encounter.

To the best of our knowledge, there is no survey paper on

SLAM-based navigation systems for BVI people. Our study

aims to bridge this gap in literature.

B. CONTRIBUTION

This paper presents a systematic literature review (SLR) ad-

dressing fundamental questions regarding SLAM-based ap-

proaches for BVI navigation. This review provides insights

into technological diversity, advantages, limitations, and the

potential to address real-world challenges. While recognizing

the broad range of potential research questions we narrowed

our focus to the four questions outlined in Table 2. The

primary contributions of this study are as follows:

•Identiﬁcation of SLAM approaches: We systematically

identiﬁed and categorized the diverse SLAM approaches

adopted in the development of assistive systems tai-

lored for visually impaired navigation. This includes

analyzing the localization and mapping techniques, sen-

sor types, computing resources, and machine-learning

methods used in these approaches.

•Advantages and limitations synthesis: Our study syn-

thesizes the advantages and limitations of these SLAM

techniques when applied to BVI navigation.

•Classiﬁcation of challenges: We identify and categorize

studies that address challenging conditions relevant to

SLAM-based navigation systems for the visually im-

paired. In addition, we discuss practical considerations

that affect the usability and adoption of these systems.

•Exploration of the potential for enhancing BVI navi-

gation: We analyzed how the proposed SLAM-based

approaches improved navigation in visually impaired

individuals. In addition, we evaluated the effectiveness

of these solutions in real-world scenarios and assessed

user satisfaction to understand their practical impact on

BVI mobility.

C. PAPER STRUCTURE

The remainder of this paper is organized as follows. In Section

II, we explain the protocol, methodology, tools, and tech-

niques used to conduct SLR. The ﬁndings of our SLR and

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Bamdad et al.: SLAM for Visually Impaired People: a Survey

answers to the SLR research questions are summarized in

Section III. Section IV presents the future opportunities and

potential advancements in this domain. Finally, Section V

concludes the paper.

II. SLR METHODOLOGY

A systematic literature review is one of the most common

types of literature review used to collect, review, appraise, and

report research studies on a speciﬁc topic, adhering to prede-

ﬁned rules for conducting the review [43]. Compared with

traditional literature reviews, it provides a wider and more

precise understanding of the topic under review [44]. Various

guidelines exist for conducting SLR in different research

ﬁelds such as software engineering [45]–[47], computer sci-

ence [48], information systems [49], planning education and

research [50], and health sciences [44], [51]. To conduct this

review, we followed the guidelines for conducting systematic

reviews proposed by [52]. Figure 2 illustrates our SLR pro-

cess.

SLR consists of three key phases: planning, conducting,

and reporting the review. We deﬁned our research questions

and motivation, keywords, and search string, as well as se-

lection criteria in the planning phase of the SLR. In the

conducting phase, we executed searches on digital sources

using predeﬁned search strings that were established during

the planning stage. We evaluated the quality of the selected

papers and extracted relevant data aligned with SLR research

questions.

We used the PICOC criteria to identify the key elements

that needed to be considered and frame our research ques-

tions. PICOC represents Population, Intervention, Compar-

ison, Outcome, and Context [53]. Table 1 lists the PICOC

elements, relevant values, and descriptions of these elements

in our study.

In this section, we ﬁrst introduce the tool we used to

manage our SLR process and then detail our methodology

for conducting our systematic literature review in planning

and conducting the review subsections.

A. SLR TOOL

Various tools have been used to conduct systematic literature

reviews. Some of them are commercial such as Covidence1,

DistillerSR2, and EPPI-Reviewer3; and some are free such

as Cadima4, Rayyan5, RevMan6, and Parsifal7. We used the

Parsifal platform to manage the SLR phases. It is an online

tool developed to support the process of performing SLR.

Parsifal provides researchers with an interface to invite co-

authors to collaborate in a shared workspace on the SLR.

1https://www.covidence.org/home

2https://www.evidencepartners.com/products/distillersr-systematic-

review-software/

3https://eppi.ioe.ac.uk/CMS/Default.aspx

4https://www.cadima.info/

5https://rayyan.qcri.org/welcome

6https://training.cochrane.org/online-learning/core-software-cochrane-

reviews/revman

7https://parsif.al

During the planning phase, this tool assists the authors by

addressing the objectives, PICOC, research questions, search

strings, keywords and synonyms, selection of sources, and

inclusion and exclusion criteria. Parsifal offers tools for creat-

ing a quality assessment checklist and data extraction forms.

In the conducting phase, this tool helps the authors import

the bibtex ﬁles and select studies. It assists in identifying

and eliminating duplicates among various sources, perform-

ing quality assessments, and facilitating data extraction from

papers. Finally, it provides a method to document the entire

SLR process.

B. PLANNING THE REVIEW

The ﬁrst step in conducting SLR is to establish a protocol.

The protocol outlines the review procedures and ensures

replicability. Within the protocol, we formulated our research

questions, designed a search strategy, and deﬁned the speciﬁc

criteria for selecting relevant studies. In addition, we deﬁned

a set of criteria presented in Table 8 to evaluate the quality of

the selected literature. Furthermore, to facilitate the extraction

of data in alignment with our research questions, we designed

a data-extraction form.

1) Research Questions and Motivation

The SLAM technique is widely used for the navigation of

robots, autonomous drones, and self-driving cars, owing to

its performance, reliability, and efﬁciency. Therefore, we re-

viewed the literature on visually impaired navigation, which

was designed based on the SLAM technique. Our aim was

to determine the advantages and limitations of employing

this technique for visually impaired navigation as well as to

identify opportunities for future research. Furthermore, we

aimed to explore how extensively this method has been used

in this speciﬁc area of research. Table 2 presents the research

questions that guided this review, and a description of the

questions.

2) Search strategy

A key step in performing SLR is to design an effective search

strategy. This strategy should be executed with reasonable

effort to retrieve relevant studies from digital libraries [54].

The exhaustive search process in systematic reviews is a

critical factor distinguishing them from traditional literature

reviews [54], leading to a wider and more precise understand-

ing of the topic under review. To design the search string we

ﬁrst extracted keywords from the PICOC elements, including

population, intervention, and outcomes. We then determined

synonyms for each keyword to broaden the search string. The

list of keywords and their synonyms related to each PICOC

element is listed in Table 3.

The ﬁrst part of the search string (i.e. ’visual* impair*’

OR ’blind’ OR ’visually disabled’ OR ’sight impaired’) is

relevant to the population element of the PICOC framework.

The addition of an asterisk to the terms ’visual’ and ’impair’

allows us to include various expressions, including ’visually

impaired’ and ’visual impairment.’ The following segment

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Bamdad et al.: SLAM for Visually Impaired People: a Survey

FIGURE 2. The process of SLR

of the query, consisting of ’navigation*’ OR ’mobility’ OR

’wayﬁnding,’ also places emphasis on the population aspect

within the context of the systematic review. The inclusion of

an asterisk in ’navigation*’ ensures comprehensive coverage,

accounting for variations such as ’navigational.’ Regarding

the Intervention component deﬁned in the PICOC framework,

we employed the term ’SLAM’ in conjunction with synonyms

identiﬁed in the literature from diverse domains where SLAM

is applied, such as robotics, autonomous driving cars, and

underwater SLAM. The last segment of the search string is

connected to the outcome element of the PICOC. Adding

keywords such as "localization" alone or the speciﬁc names

of SLAM techniques did not increase the number of related

papers. The search strings were employed on ten large citation

databases, as listed in Table 4, to carry out an exhaustive

search. We modiﬁed the base search string according to the

Search Tip in each library to satisfy speciﬁc requirements.

We utilized the Advanced Search feature in digital libraries

to gain more control over our search parameters. The title,

abstract, and keyword ﬁelds were selected to retrieve the

search results. Searching on Google Scholar is somewhat dif-

ferent from searching for other digital libraries. Unlike other

platforms, Google Scholar does not suggest various ﬁlters,

requiring the manual incorporation of ﬁlters into the search

string. Additionally, to identify English-written studies, we

adjusted the language preference settings within our Google

Scholar account to ﬁlter the search results in English.

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TABLE 1. Elements of PICOC

Element Description [53] Value

Population The problem or situation Visually impaired navigation

Intervention The technology, tool or method under study SLAM

Comparison

(optional)

The technology, tool or method with which the intervention is com-

pared

Outcome Results that Intervention could produce Lightweight, affordable, accurate, efﬁcient assistive technology

Context The speciﬁc context of the study Autonomous mobility of visually impaired people

TABLE 2. Research questions for the SLR process

No. Research question Description

RQ1 What localization and mapping approaches are used for the navigation of

the visually impaired?

The target is to identify different localization and mapping techniques

adopted for the development of assistive devices for visually impaired

navigation.

RQ2 What are the advantages and limitations of SLAM techniques for BVI

navigation?

The objective is to summarize the advantages and constraints of SLAM-

based approaches for visually impaired navigation.

RQ3 What challenging situations have been addressed? The purpose of this question is to know which of the challenging condi-

tions (e.g. crowded environment, changing view point, challenging light

conditions, dynamic objects, etc.) relevant to navigation systems have been

considered.

RQ4 How does the proposed solution improve navigation using SLAM for

individuals with impaired vision?

The research question seeks to understand how SLAM techniques can

enhance mobility and navigation in individuals with visual impairments.

TABLE 3. Keywords used to design search string

Keywords Synonyms PICOC element

Visually impaired naviga-

tion

Blind navigation, Navigation assistance for the visually impaired, Navigational guidance for individ-

uals with visual impairments, Navigational support for the visually disabled, Orientation and mobility

for the visually impaired, Sight-impaired navigation, Visual impairment navigation aid, low vision

navigation, partially sighted navigation

Population

SLAM Real-time mapping and positioning, Simultaneous Localization and Mapping, Simultaneous mapping

and position tracking, mapping and localization

Intervention

Accurate, efﬁcient, reliable

assistive technology

high-performance, precise, effective, trustworthy assistive technology Outcome

TABLE 4. Databases selected for the search procedure

Digital source Web address # of papers Last access date

ACM Digital Library http://portal.acm.org 4 22 Jul 2023

Google Scholar https://scholar.google.com 0 23 Jul 2023

IEEE Xplore http://ieeexplore.ieee.org 11 22 Jul 2023

MDPI https://www.mdpi.com 2 23 Jul 2023

PubMed https://www.ncbi.nlm.nih.gov/pubmed 3 22 Jul 2023

Science Direct http://www.sciencedirect.com 2 22 Jul 2023

Scopus http://www.scopus.com 22 23 Jul 2023

Springer Link http://link.springer.com 2 22 Jul 2023

Taley & Francis https://www.tandfonline.com 0 22 Jul 2023

Wiley Online Library https://onlinelibrary.wiley.com 1 22 Jul 2023

3) Selection criteria

Table 5 presents the selection criteria used to identify the

eligible studies during the selection process. The Availability

criterion included studies accessible in full text from digital

databases. In addition, the Language criterion ensured the

inclusion of publications written only in English. Further-

more, the Publication Period criterion restricted the inclusion

of studies published between January 2017 and July 2023.

This timeframe allowed us to prioritize the most current and

state-of-the-art approaches. The Type of Source criterion in-

cluded conference and journal papers, which were considered

peer-reviewed and academically recognized sources. Books,

dissertations, newsletters, speeches, technical reports, and

white papers were excluded. Finally, the Relevance criterion

played an important role in the exclusion process; therefore,

publications outside the scope of our study were excluded

based on a review of their titles and abstracts.

C. CONDUCTING THE REVIEW

As shown in Figure 2, the review process began after the

review protocol was ﬁnalized. The conducting phase is

a multi-stage process that includes research identiﬁcation,

study selection, data extraction, and data synthesis. In the

research identiﬁcation step, digital libraries were searched

using adapted search strings that were speciﬁc to each library.

This search aimed to collect a pool of potentially relevant

primary studies. The next step involved the selection of stud-

ies for which the relevance of each study to the review was

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TABLE 5. Selection criteria

Criteria Inclusion Exclusion

Availability Available in full text Not accessible in speciﬁc databases

Language English Not written in English

Publication period From 2017 to July 2023 Prior 2017

Type of source Conferences and journals papers Books, dissertation, newsletters, speeches, techni-

cal reports, white papers

Relevance Papers relevant to at least two research questions Outside the scope of our research

evaluated. The steps involved in this process are illustrated in

Figure. 3. During the data extraction phase, the data required

from the studies were collected and analyzed. We employed

the data extraction form established during the development

phase of the review protocol to ensure accurate extraction of

information that addresses our research questions.

1) Identification

During the initial phase of our review, we conducted searches

across the digital libraries using custom-formulated queries

for each library. For each dataset, we ran three different

search strings (SS1, SS2, and SS3), as shown in Table 6,

consisting of various combinations of keywords, booleans,

and wildcard operators. These search strings were applied

to all digital libraries except Google Scholar. For Google

Scholar, we initially used keywords similar to those used in

SS1, resulting in over 11,000 results. Upon reviewing a subset

of these, we determined that a signiﬁcant number were not

relevant to our topic. Consequently, we decided to use only

the primary keywords (shown in Table 3) to construct the

search string for this digital library.

We selected the search string that yielded the most results

to identify primary studies and then applied exclusion criteria

to the results obtained from the search strings used for each

library. We observed that SS3, which incorporated ’Orienta-

tion and mobility’ to reﬁne the search by focusing on more

specialized literature, did not yield better results than SS1,

which included the general term ’mobility’, across all digital

libraries. This indicates that the broader term ’mobility’ was

sufﬁcient to capture the necessary literature. The speciﬁcity

of SS3 did not contribute to additional relevant results. Ad-

ditionally, upon receiving the message ’Use fewer Boolean

connectors (maximum 8 per ﬁeld)’ while running SS1 on

ScienceDirect, we switched to SS2 to maintain the number

of Boolean connectors within the limit.

The initial searches of all digital libraries resulted in 6,809

records. The search strings used for each digital library is

presented in Table 7.

The results obtained from digital libraries searches were

exported in the BibTex format, a process facilitated by the

export citation features available in the libraries. The BibTex

data were then imported into the Parsifal framework for sub-

sequent stages of our review. Springer and Google Scholar

do not provide direct options for exporting data in BibTex

format. To address this issue, we used Zotero and its browser

plugin, Zotero Connector, to streamline the process. With

these tools, we added paper information from webpage views

to Zotero and subsequently retrieved BibTex data.

For Springer Link, which provides only CSV ﬁles with

search results, we opened the CSV in Excel and extracted

the DOIs. These DOIs were then pasted into Zotero’s "Add

item(s) by identiﬁer" feature. After importing the DOIs into

Zotero, we selected the appropriate folder containing the

imported papers and exported the collection to the BibTex

format using a simple right-click. As Scholar Google does not

provide easy export of a large number of records, we adopted

a similar approach: creating a library, saving search results

to that library, and exporting paper data in BibTex format

from that library. This process ensured that we obtained the

necessary data for the subsequent stages of our systematic

review.

2) Study selection

After conducting searches in the digital libraries, we applied

our selection criteria, as deﬁned in our review protocol, to

ﬁlter out irrelevant studies. Initially, records published before

2017 were excluded. Further exclusions involved ﬁltering out

publications that were not written in English or had not been

published in peer-reviewed venues. Following these steps, of

the initial 6809 records found in the initial search, 5431 were

excluded.

We imported the study data into the Parsifal platform in

BibTeX format, as explained in Section II-C1, which helped

remove duplicate studies. A total of 116 duplicate papers were

excluded. We then reviewed the titles and abstracts of the

remaining studies, excluding those irrelevant to our research

topic. In this step, 779 studies were excluded.

In the next step, we performed a fast reading of the full

text of the remaining papers, excluding 265 studies that were

outside the scope of our research. We then evaluated the

quality of the studies based on the quality assessment criteria

deﬁned in the SLR protocol. Five studies were removed

during the assessment of study quality. Table 8 lists the quality

assessment criteria for our SLR.

We carefully read 213 full-text articles to address the re-

search questions. As 166 articles were not relevant to at least

two of our research questions, they were removed, leaving 47

articles for the ﬁnal stage.

To objectively assess the performance of our search strat-

egy, we employed the quasi-gold standard (QGS) technique,

as described by [54]. Using this method, a set of articles

related to the research topic is manually selected. Digital

libraries are then searched based on the research strategy

to identify related studies. Finally, the retrieved articles are

VOLUME 11, 2023 7

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 6. Search strings applied to digital libraries, featuring keywords and operators to identify primary studies.

SS1 SS2 SS3 SS4

("Visually impaired" OR blind OR

"visual impairment*" OR "visually

disabled" OR "Sight impaired")

AND (navigation* OR mobility)

AND (SLAM OR "Simultaneous

Localization and Mapping" OR

"Real-time mapping and positioning"

OR "Simultaneous mapping and

position tracking" OR "mapping and

localization") AND (technology OR

aid OR support OR assist*)

("Visually impaired" OR blind) AND

(navigation OR mobility) AND

(SLAM OR "Simultaneous

Localization and Mapping")

("Visually impaired" OR blind OR

"visual impairment*" OR "visually

disabled" OR "Sight Impaired")

AND (navigation* OR "Orientation

and mobility") AND (SLAM OR

"Simultaneous Localization and

Mapping" OR "Real-time mapping

and positioning" OR "Simultaneous

mapping and position tracking" OR

"mapping and localization") AND

(technology OR aid OR support OR

assist*)

("conference paper" OR "journal")

AND ("Visually impaired") AND

(navigation) AND (SLAM) AND

(-review) AND ( -survey)

TABLE 7. Utilization of search strings by digital libraries

Digital source Search string Number of results

ACM Digital Library SS1 284

Google Scholar SS4 602

IEEE Xplore SS2 50

MDPI SS2 191

PubMed SS1 8

Science Direct SS2 518

Scopus SS2 1510

Springer Link SS2 2585

Taley & Francis SS2 486

Wiley Online Library SS2 575

TABLE 8. Quality assessment criteria and weights

Criteria Weight

Is there an adequate description of the context in

which the research was carried out?

0.0, 0.5, 1.0

Does the methodology take into account both local-

ization and mapping issues?

0.0, 0.1, 2.0

Is the solution proposed well presented? 0.0, 0.5, 1.0

Is there a clear statement of ﬁndings? 0.0, 0.5, 1.0

Is the research design appropriate to address the aims

of the research?

0.0, 0.5, 1.0

Does the study add value to the research community? 0.0, 0.5, 1.0

compared with QGS, and the sensitivity of the search strategy

is calculated using the following formula:

Sensitivity =Number of relevant studies retrieved

Total number of relevant studies ×100

In our SLR, with 30 manually selected relevant studies and

48 studies retrieved using the SLR search strategy, of which

26 were among the manually selected studies, the resulting

sensitivity was approximately 86.67%.

To provide a broader range of relevant studies, we included

papers on the forward snowballing process [55]. This pro-

cess involves identifying and accessing references in a paper

and reviewing cited papers. We used "Cited by" feature of

Google Scholar to identify these additional papers. In this

stage, 695 articles were identiﬁed. After removing duplicates

and applying selection criteria similar to those used for the

articles obtained from digital libraries, we added seven more

articles to the ﬁnal collection. Consequently, 54 articles were

included in this review. Figure 3 shows a diagram of the study-

selection process.

FIGURE 3. Studies selection process

It is important to note that the last search conducted in

digital libraries was on July 23, 2023, and that for forward

snowballing was on August 12, 2023. These dates should be

considered as the starting points for future reviews. The pub-

lications included in our review are listed in Tables 9–11 and

categorized based on their publication venues. These tables

provide an overview of the literature, including the paper title,

author names, publication year, location, and source through

8VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

which the studies were discovered. Among the 54 studies

included in our SLR, 27 were sourced from journals, as

presented in Table 9. The remaining 27 studies were presented

at conferences, as shown in Tables 10 and 11.

Additionally, Tables 12–15 summarize the perspectives

and innovations presented in the publications with insights

into their limitations and advantages. These tables demon-

strate the research issues addressed and the contributions of

each study, highlighting the strengths and potential draw-

backs of the proposed solutions. They also indicate which

solutions are open source, with only seven papers having

some or all parts of the project available as open source. Links

to the sources are provided in these tables if they are directly

available in the relevant papers.

3) Data extraction

Data extraction is a critical phase in the systematic literature

review process in which relevant data from selected studies

are systematically collected. To achieve this objective, we

employed the data-extraction form deﬁned in the SLR proto-

col. This form consists of various ﬁelds designed to retrieve

answers to our research questions from each of the included

articles. Within the scope of this SLR, we deﬁned the follow-

ing essential elements, each contributing to a comprehensive

understanding of the reviewed literature:

•Short summary of the paper: A concise overview of the

main points and ﬁndings of the study.

•Research issue and contribution: Summary of the re-

search issues addressed and contributions of the studies.

•Localization and mapping technique: Identiﬁcation of

speciﬁc techniques applied for localization and map-

ping.

•Localization and mapping accuracy and robustness: As-

sessment of accuracy and robustness levels in localiza-

tion and mapping techniques.

•Running time: Analysis of the running time for localiza-

tion and mapping techniques.

•Advantages of the presented method: The strengths as-

sociated with the localization method presented in each

paper for visually impaired navigation.

•Limitations of the presented method: Identiﬁcation of

weaknesses or constraints associated with the localiza-

tion technique.

•Types of obstacles addressed: Categorization of obsta-

cles, static and dynamic, as a challenge during naviga-

tion.

•Challenging conditions: Explanation of other challeng-

ing scenarios that the methods are designed to handle.

•Types of sensors: Identiﬁcation of sensors employed to

receive data from the surroundings.

•Computing resources: Identiﬁcation of computing re-

sources used in SLAM-based solutions.

•Improvement in navigation: Identiﬁcation of how

SLAM-based methods enhance navigation for individ-

uals with impaired vision.

•Working area: Whether the method is intended for in-

door, outdoor, or both indoor and outdoor environments.

•Practical challenges and operational efﬁciency: Eval-

uation of the user-friendliness, cost-efﬁciency, weight,

comfort for extended use, adjustable ﬁt, fatigue mitiga-

tion, and portability of the SLAM-based assistive tools.

•System prototype information: Detailed information on

functionalities, sensors, computing resources, human-

computer interaction (HCI) mechanisms, assistive tools,

and battery life.

•User evaluation: Assessment of user satisfaction of the

SLAM-based assistive tools.

•Machine learning techniques: Identiﬁcation of machine

learning techniques used in assistive solutions.

•Open-source availability: Identiﬁcation of open-source

contributions in the reviewed studies.

•Possible future opportunities and directions: Exploration

of potential future research areas and directions stem-

ming from these ﬁndings.

•The research questions addressed: Identiﬁcation of spe-

ciﬁc SLR research questions addressed by each study.

VOLUME 11, 2023 9

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 9. List of journal publications included in SLR

Ref. Title Authors Published Year Source

[56] Soniﬁcation of navigation instructions for people with visual

impairment

Dragan Ahmetovic and Federico Avanzini and Adriano

Baratè and Cristian Bernareggi and Marco Ciardullo and

Gabriele Galimberti and Luca A. Ludovico and Sergio Ma-

scetti and Giorgio Presti

International Journal

of Human-Computer

Studies

2023 Science@Direct

[57] Sensing and Navigation of Wearable Assistance Cognitive

Systems for the Visually Impaired

Li, Guoxin and Xu, Jiaqi and Li, Zhijun and Chen, Chao and

Kan, Zhen

IEEE Transactions

on Cognitive and

Developmental

Systems

2023 IEEE Digital

Library

[58] Mixture reality-based assistive system for visually impaired

people

Jucheng Song and Jixu Wangand Shuliang Zhu and Haidong

Hu and Mingliang Zhai and Jiucheng Xie and Hao Gao

Displays 2023 Science@Direct

[59] Research on Design and Motion Control of a Considerate

Guide Mobile Robot for Visually Impaired People

Zhang, Bin and Okutsu, Mikiya and Ochiai, Rin and Tayama,

Megumi and Lim, Hun-Ok

IEEE Access 2023 Scopus

[60] UNav: An Infrastructure-Independent Vision-Based Navi-

gation System for People with Blindness and Low Vision

Yang, Anbang and Beheshti, Mahya and Hudson, Todd

E and Vedanthan, Rajesh and Riewpaiboon, Wachara and

Mongkolwat, Pattanasak and Feng, Chen and Rizzo, John-

Ross

Sensors 2022 PubMed

[61] Multi-Floor Indoor Localization Based on Multi-Modal Sen-

sors

Zhou, Guangbing and Xu, Shugong and Zhang, Shunqing

and Wang, Yu and Xiang, Chenlu

Sensors 2022 Scopus

[62] Knowledge driven indoor object-goal navigation aid for vi-

sually impaired people

Hou, Xuan and Zhao, Huailin and Wang, Chenxu and Liu,

Huaping

Cognitive Computa-

tion and Systems

2022 Wiley Online

Library

[63] Indoor-Guided Navigation for People Who Are Blind:

Crowdsourcing for Route Mapping and Assistance

Plikynas, Darius and Indriulionis, Audrius and Laukaitis,

Algirdas and Sakalauskas, Leonidas

Applied Sciences

(Switzerland)

2022 Scopus

[64] A Multi-Sensory Guidance System for the Visually Impaired

Using YOLO and ORB-SLAM

Xie, Zaipeng and Li, Zhaobin and Zhang, Yida and Zhang,

Jianan and Liu, Fangming and Chen, Wei

Information 2022 MDPI

[65] Egocentric Human Trajectory Forecasting with a Wearable

Camera and Multi-Modal Fusion

Qiu, Jianing and Chen, Lipeng and Gu, Xiao and Lo, Frank

P.-W. and Tsai, Ya-Yen and Sun, Jiankai and Liu, Jiaqi and

Lo, Benny

IEEE Robotics and

Automation Letters

2022 Scopus

[66] A wearable navigation device for visually impaired people

based on the real-time semantic visual slam system

Chen, Zhuo and Liu, Xiaoming and Kojima, Masaru and

Huang, Qiang and Arai, Tatsuo

Sensors 2021 PubMed

[67] Multimodal sensing and intuitive steering assistance im-

prove navigation and mobility for people with impaired

vision

Slade, Patrick and Tambe, Arjun and Kochenderfer, Mykel

Science Robotics 2021 Scopus

[68] Assistive Navigation Using Deep Reinforcement Learning

Guiding Robot With UWB/Voice Beacons and Semantic

Feedbacks for Blind and Visually Impaired People

Lu, Chen-Lung and Liu, Zi-Yan and Huang, Jui-Te and

Huang, Ching-I and Wang, Bo-Hui and Chen, Yi and Wu,

Nien-Hsin and Wang, Hsueh-Cheng and Giarré, Laura and

Kuo, Pei-Yi

Frontiers in Robotics

and AI

2021 Scopus

[69] Indoor WearableNavigation System Using 2D SLAM Based

on RGB-D Camera for Visually Impaired People

Hakim, Heba and Fadhil, Ali Advances in

Intelligent Systems

and Computing

2021 Scopus

[70] An RGB-D Camera Based Visual Positioning System for

Assistive Navigation by a Robotic Navigation Aid

Zhang, He and Jin, Lingqiu and Ye, Cang IEEE/CAA Journal

of Automatica Sinica

2021 IEEE Digital

Library

[71] Hierarchical visual localization for visually impaired people

using multimodal images

Cheng, Ruiqi and Hu, Weijian and Chen, Hao and Fang,

Yicheng and Wang, Kaiwei and Xu, Zhijie and Bai, Jian

Expert Systems with

Applications

2021 Scopus

[72] Indoor Topological Localization Based on a Novel Deep

Learning Technique

Liu, Qiang and Li, Ruihao and Hu, Huosheng and Gu,

Dongbing

Cognitive Computa-

tion

2020 Scopus

[73] Combining Obstacle Avoidance and Visual Simultaneous

Localization and Mapping for Indoor Navigation

Jin, SongGuo and Ahmed, Minhaz Uddin and Kim, Jin Woo

and Kim, Yeong Hyeon and Rhee, Phill Kyu

Symmetry 2020 MDPI

[74] Wearable travel aid for environment perception and naviga-

tion of visually impaired people

Bai, Jinqiang and Liu, Zhaoxiang and Lin, Yimin and Li, Ye

and Lian, Shiguo and Liu, Dijun

Electronics (Switzer-

land)

2019 Scopus

[75] An ARCore based user centric assistive navigation system

for visually impaired people

Zhang, Xiaochen and Yao, Xiaoyu and Zhu, Yi and Hu, Fei Applied Sciences

(Switzerland)

2019 Scopus

[76] Virtual-Blind-Road Following-Based Wearable Navigation

Device for Blind People

Bai, Jinqiang and Lian, Shiguo and Liu, Zhaoxiang and

Wang, Kai and Liu, Dijun

IEEE Transactions

on Consumer

Electronics

2018 IEEE Digital

Library

[77] An indoor wayﬁnding system based on geometric features

aided graph SLAM for the visually impaired

Zhang, He and Ye, Cang IEEE Transactions

on Neural Systems

and Rehabilitation

Engineering

2017 PubMed

[78] Plane-Aided Visual-Inertial Odometry for 6-DOF Pose Es-

timation of a Robotic Navigation Aid

Zhang, He and Ye, Cang IEEE Access 2020 Scopus

[79] SRAVIP: Smart Robot Assistant for Visually Impaired Per-

sons

Albogamy, Fahad and Alotaibi, Turk and Alhawdan, Ghalib

and Mohammed, Faisal

International Journal

of Advanced

Computer Science

and Applications

2021 Forward

Snowballing

[80] A Lightweight Approach to Localization for Blind and Vi-

sually Impaired Travelers

Crabb, Ryan and Cheraghi, Seyed Ali and Coughlan, James

Sensors 2023 Forward

Snowballing

[82] Wearable system to guide crosswalk navigation for people

with visual impairment

Son, Hojun and Weiland, James Frontiers in Electron-

ics

2022 Forward

Snowballing

[83] Indoor Low Cost Assistive Device using 2D SLAM Based

on LiDAR for Visually Impaired People

Hakim, Heba and Fadhil, Ali Iraqi Journal

for Electrical

& Electronic

Engineering

2019 Forward

Snowballing

10 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 10. List of conference papers included in SLR - part 1.

Ref. Title Authors Published Year Source

[84] Efﬁcient Real-Time Localization in Prior Indoor

Maps Using Semantic SLAM

Goswami, R. G. and Amith, P. V. and Hari, J. and

Dhaygude, A. and Krishnamurthy, P. and Rizzo, J.

and Tzes, A. and Khorrami, F.

9th Inter. Conf.

on Automation,

Robotics and

Applications

(ICARA)

2023 IEEE Digi-

tal Library

[85] Detect and Approach: Close-Range Navigation Sup-

port for People with Blindness and Low Vision

Hao, Yu and Feng, Junchi and Rizzo, John-Ross and

Wang, Yao and Fang, Yi

European Conf. on

Computer Vision

2022 Springer

Link

[87] PathFinder: Designing a Map-Less Navigation Sys-

tem for Blind People in Unfamiliar Buildings

Kuribayashi, Masaki and Ishihara, Tatsuya and Sato,

Daisuke and Vongkulbhisal, Jayakorn and Ram,

Karnik and Kayukawa, Seita and Takagi, Hironobu

and Morishima, Shigeo and Asakawa, Chieko

CHI Conf. on

Human Factors

in Computing

Systems

2023 ACM Digi-

tal Library

[88] A Novel Perceptive Robotic Cane with Haptic Nav-

igation for Enabling Vision-Independent Participa-

tion in the Social Dynamics of Seat Choice

Agrawal, Shivendra and West, Mary Etta and Hayes,

Bradley

IEEE Inter. Conf.

on Intelligent

Robots and

Systems

2022 Scopus

[89] A Multi-Sensory Blind Guidance System Based on

YOLO and ORB-SLAM

Rui, Chufan and Liu, Yichen and Shen, Junru and Li,

Zhaobin and Xie, Zaipeng

IEEE Inter. Conf.

on Progress in

Informatics and

Computing (PIC)

2021 IEEE Digi-

tal Library

[90] Indoor Navigation Assistance for Visually Impaired

People via Dynamic SLAM and Panoptic Segmenta-

tion with an RGB-D Sensor

Ou, Wenyan and Zhang, Jiaming and Peng, Kunyu

and Yang, Kailun and Jaworek, Gerhard and Müller,

Karin and Stiefelhagen, Rainer

Inter. Conf.

on Computers

Helping People

with Special

Needs

2022 Scopus

[91] A Wearable Robotic Device for Assistive Navigation

and Object Manipulation

Jin, Lingqiu and Zhang, He and Ye, Cang IEEE Inter. Conf.

on Intelligent

Robots and

Systems

2021 Scopus

[92] Multi-functional smart E-glasses for vision-based in-

door navigation

Xu, Jiaqi and Xia, Haisheng and Liu, Yueyue and Li,

Zhijun

Inter. Conf.

on Advanced

Robotics and

Mechatronics

(ICARM)

2021 Scopus

[93] Personalized Navigation that Links Speaker’s Am-

biguous Descriptions to Indoor Objects for Low Vi-

sion People

Lu, Jun-Li and Osone, Hiroyuki and Shitara, Akihisa

and Iijima, Ryo and Ryskeldiev, Bektur and Sarcar,

Sayan and Ochiai, Yoichi

Inter. Conf. on

Human-Computer

Interaction

2021 Springer

Link

[95] Guiding Blind Pedestrians in Public Spaces by Un-

derstanding WalkingBehavior of Nearby Pedestrians

Kayukawa, Seita and Ishihara, Tatsuya and Tak-

agi, Hironobu and Morishima, Shigeo and Asakawa,

Chieko

Proc. ACM

Interact. Mob.

Wearable

Ubiquitous

Technol.

2020 ACM Digi-

tal Library

[96] A Navigation Aid for Blind People Based on Visual

Simultaneous Localization and Mapping

Chen, Cing-Han and Wang, Chien-Chun and Lin,

Sian-Fong

IEEE Inter. Conf.

on Consumer

Electronics

2020 IEEE Digi-

tal Library

[97] Can we unify perception and localization in assisted

navigation? an indoor semantic visual positioning

system for visually impaired people

Chen, Haoye and Zhang, Yingzhi and Yang, Kailun

and Martinez, Manuel and Müller, Karin and Stiefel-

hagen, Rainer

Computers

Helping People

with Special

Needs: 17th Inter.

Conf., ICCHP

2020 Scopus

[98] Indoor Localization for Visually Impaired Travelers

Using Computer Vision on a Smartphone

Fusco, Giovanni and Coughlan, James M. 17th Inter. web for

all Conf.

2020 ACM Digi-

tal Library

[99] Human-Robot Interaction for Assisted Wayﬁnding

of a Robotic Navigation Aid for the Blind

Zhang, He and Ye, Cang Inter. Conf. on Hu-

man System Inter-

action, HSI

2019 Scopus

[100] A Multi-Sensor Fusion System for Improving Indoor

Mobility of the Visually Impaired

Zhao, Yu and Huang, Ran and Hu, Biao Chinese

Automation

Congress (CAC)

2019 IEEE Digi-

tal Library

[101] Navigation Agents for the Visually Impaired: A Side-

walk Simulator and Experiments

Weiss, Martin and Chamorro, Simon and Girgis,

Roger and Luck, Margaux and Kahou, Samira E. and

Cohen, Joseph P. and Nowrouzezahrai, Derek and

Precup, Doina and Golemo, Florian and Pal, Chris

Proceedings of

Machine Learning

Research

2019 Scopus

[102] Real-time localization and navigation in an indoor

environment using monocular camera for visually

impaired

Ramesh, Kruthika and Nagananda, S. N. and Ra-

masangu, Hariharan and Deshpande, Rohini

5th Inter. Conf.

on Industrial

Engineering and

Applications

(ICIEA)

2018 IEEE Digi-

tal Library

VOLUME 11, 2023 11

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 11. List of conference papers included in SLR - part 2.

Ref. Title Authors Published Year Source

[103] Indoor Navigation using Text Extraction Eden, Jake and Kawchak, Thomas and Narayanan, Vijaykr-

ishnan

IEEE Inter.

Workshop on Signal

Processing Systems

(SiPS)

2018 IEEE Digital

Library

[104] Autonomous Scooter Navigation for People with Mobility

Challenges

Mulky, Rajath Swaroop and Koganti, Supradeep and Shahi,

Sneha and Liu, Kaikai

IEEE Inter. Conf.

on Cognitive

Computing (ICCC)

2018 IEEE Digital

Library

[105] Localizing people in crosswalks using visual odometry: Pre-

liminary results

Lalonde, Marc and St-Charles, Pierre-Luc and Loupias,

Délia and Chapdelaine, Claude and Foucher, Samuel

Inter. Conf. on Pat-

tern Recognition Ap-

plications and Meth-

ods (ICPRAM)

2018 Scopus

[106] Plane-aided visual-inertial odometry for pose estimation of

a 3D camera based indoor blind navigation

Zhang, He and Ye, Cang British Machine Vi-

sion Conf. (BMVC)

2017 Scopus

[107] CCNY Smart Cane Chen, Qingtian and Khan, Muhammad and Tsangouri,

Christina and Yang, Christopher and Li, Bing and Xiao,

Jizhong and Zhu, Zhigang

IEEE 7th Annual

Inter. Conf. on

CYBER Technology

in Automation,

Control, and

Intelligent Systems

(CYBER)

2017 IEEE Digital

Library

[108] A Cloud and Vision-Based Navigation System Used for

Blind People

Bai, Jinqiang and Liu, Dijun and Su, Guobin and Fu,

Zhongliang

Inter. Conf. on artiﬁ-

cial intelligence, au-

tomation and control

technologies

2017 ACM Digital

Library

[109] Indoor positioning and obstacle detection for visually im-

paired navigation system based on LSD-SLAM

Endo, Yuki and Sato, Kei and Yamashita, Akihiro and Mat-

subayashi, Katsushi

Inter. Conf. on

Biometrics and

Kansei Engineering

(ICBAKE)

2017 Scopus

[110] SeeWay: Vision-Language Assistive Navigation for the Vi-

sually Impaired

Yang, Zongming and Yang, Liang and Kong, Liren and Wei,

Ailin and Leaman, Jesse and Brooks, Johnell and Li, Bing

IEEE Inter. Conf. on

Systems, Man, and

Cybernetics (SMC)

2022 Forward

Snowballing

[111] The Methods of Visually Impaired Navigating and Obstacle

Avoidance

Shahani, Siddharth and Gupta, Nitin Inter. Conf. on Ap-

plied Intelligence and

Sustainable Comput-

ing (ICAISC)

2023 Forward

Snowballing

[112] The Design of Person Carrier Robot using SLAM and Ro-

bust Salient Detection

Yun, Youngjae and Gwon, Taeyang and Kim, Donghan 18th Inter. Conf. on

Control, Automation

and Systems

(ICCAS)

2018 Forward

Snowballing

12 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

III. RESULT

In this section, the ﬁndings of SLR are presented. Figure 4

shows the number of studies included in this review, which fo-

cused on BVI navigation using SLAM techniques. As shown

in the ﬁgure, although only papers published in the ﬁrst half of

2023 are included in this review, they constitute a substantial

portion of the total. The growth in the number of studies in this

domain suggests an advancement in SLAM techniques and an

increase in their usage for developing navigation technologies

for visually impaired individuals. This section is divided into

four parts to answer the research questions. It discusses the

types of SLAM techniques used to develop assistive tech-

nologies for visually impaired navigation, delves into the

advantages and limitations of these techniques, highlights the

challenging scenarios addressed, and presents the attributes

of the SLAM technology that contribute to the enhancement

of visually impaired navigation.

FIGURE 4. Publications included in this review on SLAM-based BVI

navigation, by year

A. RQ1. WHAT LOCALIZATION AND MAPPING

APPROACHES ARE USED FOR THE NAVIGATION OF THE

VISUALLY IMPAIRED?

Since there are many different types of SLAMs designed with

different types of sensors, for different applications, and for

different scenarios, in this section, we focus on reviewing the

types of SLAM that have been used for the navigation of

visually impaired people. It is a key technology in robotics

and computer vision and has the potential to assist visually

impaired individuals with navigation. This can help visually

impaired individuals provide real-time location information,

maps, and spatial awareness. Among the 54 studies surveyed,

three strategies were common, as shown in Table 16. In this

table, we use the exact terms mentioned in the literature for

the localization and mapping techniques.

To further understand the technical features employed in

these solutions, the detailed information is presented in Tables

17–19. These tables focus on the localization and mapping

components of the assistive system, speciﬁcally highlighting

the sensor types, computing resources, and application of ma-

chine learning-based methods. By examining these features,

we can gain deeper insight into how these systems are struc-

tured and the diverse technologies utilized to achieve accurate

and efﬁcient SLAM for assistive navigation. It is important to

note that this information relates only to the localization and

mapping components of the assistive navigation solutions.

Details of the entire system are provided in Tables 33–37.

This section is divided into three subsections, where we

discuss the localization and mapping approaches, the sensor

types used for these approaches, and the computing resources

required to perform these approaches.

1) Approaches

The majority of studies have leveraged established SLAM

techniques, such as ORB-SLAM, while some studies have

developed new solutions tailored to their needs. For example,

[73] proposed visual simultaneous localization and mapping

for the moving-person tracking (VSLAMMPT) method. The

proposed method was designed to assist people with disabili-

ties, particularly visually impaired individuals, in navigating

indoor environments. Additionally, various studies have used

the SLAM components of existing frameworks, such as AR-

Core and ZED camera SLAM.

It is worth noting that several studies have employed VIO

and SLAM as the core components in their proposed systems,

whereas others have employed them to enhance the robust-

ness of localization [71], for comparison with alternative

localization approaches [68], or to develop new localization

methods [80]. For example, [80] presented a novel local-

ization approach speciﬁcally designed for individuals with

visual impairment. This method combines visual landmark

identiﬁcation, VIO, and spatial constraints derived from a

two-dimensional (2D) ﬂoor plan.

SLAM methodologies can be categorized into feature-

based, direct, and optical-ﬂow techniques. Feature-based

methods extract and describe feature points in an image,

which are then matched across different images for tracking

and mapping. On the other hand, direct methods directly

calculate the luminosity changes of pixel blocks. Optical ﬂow

methods utilize the optical ﬂow changes in feature points,

pixel gradient points, or the entire image to track and map

the environment [66].

SLAM algorithms are further categorized into optimization-

and ﬁltering-based methods, each with distinct approaches

to map creation and agent localization. Optimization-based,

often referred to as Graph SLAM, treats the problem as a large

optimization task, where the goal is to ﬁnd the set of poses

and landmark positions that best explain the observed sensor

measurements. This is typically achieved by constructing a

graph in which nodes represent agent poses or landmarks

and edges represent constraints or observations between

them. The solution is determined by minimizing the global

cost function, which represents the error between the pre-

dicted and actual measurements, using nonlinear optimization

techniques. On the other hand, ﬁltering-based SLAM uses

recursive Bayesian ﬁlters, such as the Extended Kalman Filter

VOLUME 11, 2023 13

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 12. A summary of perspectives and innovations in SLAM-based navigation solutions, with insights into limitations and advantages - Part I.

Ref. Open-source Research Issue Contribution Advantages Limitations

[56] •Addressing soniﬁcation techniques

for navigation instruction

Innovative soniﬁcation for BVI nav-

igation

Publicly available localization ap-

proach

Prone to drift, utilizing markers for

user positioning

[57] •Enhancing indoor navigation for

BVI with wearable technologies

Low-cost wearable, SLAM naviga-

tion, multitarget recognition

Non-intrusive wearable device, Map

reuse,

Network impact, cognitive feedback

needs

[58] •Improving perception and indepen-

dent task completion for BVI

Mixed reality, real-time perception,

remote assistance

Improves perception, functions in di-

verse indoors, advanced interactive

platform, sensor integration

Network status inﬂuence

[59] •Addressing guide robots’ lack of

consideration for user status and ob-

stacle properties

Introduced considerate robot design

and spatial risk map for navigation

Robot adapts speed and movement,

enhancing natural interaction

without disturbing others; calculates

pedestrian directions

Needs improved speech recognition,

real-world testing with BVI, effec-

tive environmental interaction

[60] •Addressing limitations of sensor-

based navigation for BVI

Infrastructure-independent vision-

based navigation for BVI

Map-evolution feedback loop en-

sures dynamic updates, and ofﬂine

computation allows for continued

use during signal loss.

Requires a dense reference image

database; difﬁcult to adapt to diverse

environments.

[61] •Improving high-precision indoor lo-

calization in complex multi-ﬂoor en-

vironments

Hybrid localization framework com-

bining visual and wireless signals for

high-precision indoor localization

Multi-modal sensor integration,

fusion-based localization, and GAN-

based approach for efﬁcient, high-

precision multi-ﬂoor localization

Implementation complexity, low po-

sitioning accuracy, and dependence

on ﬁngerprint database and ofﬂine

maps

[62] •Applying object-goal navigation to

aid BVI in indoor settings

Migration of object-goal navigation

to assistive devices and a knowledge-

driven approach

Active assistance with context-

aware, knowledge-driven navigation

for improved indoor object-goal

guidance

Dependency on unlabelled images,

scene understanding complexity, and

generalization to unfamiliar environ-

ments.

[63] •Operationalizing Web 2.0 social net-

working for indoor navigation assis-

tance

Innovative integration of Crowd-

sourcing and social networking for

indoor navigation

Constant access to a 24/7 indoor

route database, ofﬂine functionality,

and ﬂexible, user-friendly wearable

devices

Relies heavily on social networking,

high processing power, and stable In-

ternet connectivity

[64] •Enhancing guidance systems for BVI

with multi-sensory integration for

improved indoor navigation

Integration of ORB-SLAM with

YOLO, dense map generation, and

practical prototype implementation

Obstacle avoidance with multi-

sensory feedback, dense navigation

maps, and real-time target detection,

implemetation of a practical smart

cane

Dense map may not always align

with reality, pathﬁnding has high

computational costs, and target de-

tection is trained based on a generic

dataset.

[65] Trajectory

dataset 1Forecasting egocentric camera wear-

ers’ trajectories in crowded spaces

A new egocentric dataset, a

Transformer-based model with

cascaded cross-attention, and

demonstration of socially compliant

robot navigation

An egocentric view with multi-

modal fusion for trajectory forecast-

ing; Socially compliant robot navi-

gation and assists visually impaired

individuals.

Not mentioned

[66] •limited navigation options, wear-

able devices, semantic visual SLAM,

cost-effective solutions

Semantic SLAM integration, real-

time solution, efﬁcient resource allo-

cation

Real-time semantic understanding,

efﬁcient resource allocation,

enhanced navigation accuracy, voice

broadcast for destination assistance

Remote server challenges including

internet access dependency, security,

and privacy concerns

[67] ✓Decreasing cognitive burden, in-

creasing walking speed with a mul-

timodal augmented cane

Improving mobility with sensors, in-

tuitive steering, and advanced nav-

igation features, increasing walking

speed

Enhanced mobility, faster walking,

precise steering, reduced cognitive

load, fewer collisions, increased con-

ﬁdence, reliable obstacle avoidance,

failure backup, selecting preferred

walking speed by user

Heavy, requiring mechanical assem-

bly

[68] •Enhancing navigation with improved

robotic assistance in dynamic envi-

ronments.

A novel haptic-guided robot with en-

hanced navigation, dynamic obsta-

cle avoidance via UWB, and voice-

enabled beacon feedback

Enhanced environmental

information, intuitive navigation,

precise UWB positioning, semantic

feedback, and DRL-based obstacle

avoidance.

Dynamic obstacles impact SLAM

accuracy; implicit learning in the

simulator may cause real-world un-

certainties.

[69] •Efﬁcient indoor navigation aid. Multi sensor utilization, efﬁcient al-

gorithm utilization, and real-time

voice guidance.

Cost-effective, real-time navigation,

accurate mapping, obstacle detec-

tion, efﬁcient path planning.

Limited to static, simple environ-

ments.

[70] •Enhancing navigation accuracy and

robustness in large indoor spaces for

assistive technologies.

Introduced DVIO for enhanced 6-

DOF pose estimation and VPS for

accurate assistive navigation.

Enhanced pose accuracy, real-time

updates, effective wayﬁnding, obsta-

cle avoidance, and signiﬁcant pose

error reduction.

Pose drift remains an issue in com-

plex environments.

1https://github.com/Jianing-Qiu/TISS (Accessed on 3 May 2024)

(EKF) or Particle Filter, to incrementally update the map and

the agent’s position as new sensor data arrive.

Systems such as ORB-SLAM and RTAB-Map are feature-

and optimization-based, employing features and graph op-

timization for mapping and localization. Conversely, LSD-

SLAM and DSO are examples of direct and optimization-

based SLAM. Some systems, such as Semantic SLAM, may

adopt either approach, depending on their implementation.

It is important to note that the method and type of SLAMs

are not directly mentioned in all papers, so the information

provided here is a general categorization based on common

practices within each category.

The reviewed studies demonstrate the versatility of SLAM

in various navigation scenarios. The speciﬁc implementation

and aspects addressed in each study varied depending on

the application. SLAM can be employed in environments

that lack a map and can dynamically create it while navi-

gating. This involves simultaneous map creation and local-

ization within an environment. Alternatively, SLAM can be

employed to generate maps for subsequent navigation. In

this case, SLAM ﬁrst builds a map of the environment and

then the map is utilized during navigation. Studies have also

14 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 13. A summary of perspectives and innovations in SLAM-based navigation solutions, with insights into limitations and advantages - Part II.

Ref. Open-source Research Issue Contribution Advantages Limitations

[71] •Improving visual localization in

challenging outdoor settings.

Uniﬁed Dual Desc network enhanc-

ing descriptor extraction and multi-

modal integration for assistive local-

ization.

Enhances robustness using multi-

modal imaging, advanced descrip-

tors, and sequential integration for

outdoor navigation.

Lacks real-time execution capability

in assistive device.

[72] •Enhancing independence for visu-

ally impaired through semantic en-

vironmental understanding and nav-

igation.

Integrates ConvNets for semantic

mapping; enhances topological lo-

calization.

Enhanced accuracy, semantic guid-

ance, robustness to changes.

Computational inefﬁciency, subopti-

mal performance in motion blur ef-

fects.

[73] •Addressing the lack of comprehen-

sive solutions for indoor navigation

and obstacle avoidance.

Integration of dynamic person-

detection method (EER–ASSL) and

VSLAM for real-time navigation

assistance in cluttered environments.

Enhanced smooth movement, reli-

able obstacle avoidance, effective

navigation in dynamic environments.

Limited instruction capabilities, the

decrease in person detection perfor-

mance under varying lighting condi-

tions and speed.

[74] •Addressing lack of integrated nav-

igation and object recognition sys-

tems.

Integration of lightweight CNN-

based object recognition and visual

SLAM for improved environment

perception and navigation.

Load cognitive decrease, safe and

quick navigation, enhanced percep-

tion, and real-time performance on

smartphones.

Unable to detect small-size obstacles

and obstacle detection limitations.

[75] •Addressing lack of user-centric in-

door navigation aids for visually im-

paired.

ARCore integration, adaptive path

planning, and dual-channel user in-

teraction for indoor navigation.

Enhanced mapping, obstacle-

avoiding path planning, and

intuitive dual-channel interaction for

improved indoor navigation.

Reliance on existing indoor scenario

CAD maps.

[76] •Addressing gaps in localization,

way-ﬁnding, and route following for

visually impaired navigation.

Dynamic subgoal route following,

visual SLAM integration, and wear-

able optical see-through glasses for

enhanced indoor navigation.

Enhances precision with visual

SLAM, cost-effective sensors,

efﬁcient obstacle avoidance, safe

indoor navigation with dynamic

subgoal selection.

Not mentioned

[77] •Addressing accumulative pose error,

GPS-denied navigation, and real-

time pose estimation.

New 6-DOF pose estimation method

using ﬂoor and wall information, and

a real-time wayﬁnding system.

Reduces accumulative pose error and

provides real-time wayﬁnding

System less effective in simple tasks,

weight causes discomfort, and fails

at high walking speeds over 0.6 m/s.

[78] •Addressing accurate 6-DOF pose es-

timation challenges through inno-

vative visual-inertial odometry for

robotic navigation aids.

A plane-aided VIO method and

a plane-consistency check for en-

hanced pose estimation accuracy.

Improved accuracy, a plane-

consistency check, practical

implementation for assistive

navigation, and outperformance

of state-of-the-art methods.

Not mentioned

[79] •Enhancing navigation in an

indoor public environment for

pre-scheduled tasks with user-

independent robotic solutions.

Innovative user-independent robotic

assistance for indoor navigation.

Enhancing inclusivity and

efﬁciency: user-independent robotic

assistance.

It is not mentioned how the proposed

approach handles crowds in the pub-

lic places under study.

[80] ICOSR

repository 2Developing a lightweight indoor lo-

calization system using a 2D ﬂoor

plan of the environment rather than

a 3D model.

Innovative localization algorithm in-

tegrating visual landmarks, VIO, and

2D ﬂoor plans.

Smartphone-based, lightweight, and

robust localization approach.

Noisy distance estimates due to im-

precise bounding boxes.

[82] Raw data

supporting

the

conclusion

Addressing the need to ensure safe

street crossing

Introducing a comprehensive wear-

able system for safe urban naviga-

tion, integrating real-time computer

vision and prior maps.

Utilizes pre-built LiDAR maps, such

as those publicly available and cre-

ated for autonomous vehicles, sup-

ports user’s preferred walking speed.

Dependent on speciﬁc crosswalk

textures, lacks dynamic obstacle

handling, may face instability with

changing features, and outdoor noise

interference.

[83] •Integration of navigation and object

recognition.

Integration of navigation, object

recognition, and low-cost sensors.

Integrated navigation and object

recognition, low-cost sensors,

efﬁcient path planning, accurate

real-time object identiﬁcation in

static scenarios.

Restricted to static simple environ-

ments.

[84] •Enhancing real-time global indoor

localization using Semantic SLAM

and a priori maps for GPS-deprived

environments.

Implementing vector-based semantic

extraction from ﬂoor plans, efﬁcient

particle ﬁlter localization, and lever-

aging loop closures for active seman-

tic point cloud.

Real-time, accurate localization; in-

tegration of semantic information;

using deep learning for enhanced ro-

bustness and efﬁciency.

Visual aliasing, limited semantic

classes.

[85] •Addressing navigation challenges

for BVI by developing a wearable

solution for real-time guidance to

target objects in unfamiliar settings.

Introducing a wearable navigation

system with real-time object local-

ization, visual SLAM, and trajectory

estimation for efﬁcient user guid-

ance.

High accuracy, vision-based, real-

time assistance, continuous object

tracking, and portable system design.

Not explicitly mentioned.

2https://www.openicpsr.org/openicpsr/project/183714/version/V1/view (Accessed on 3 May 2024)

used SLAM odometry for navigational tracking. Odometry

provides a continuous estimate of the position and orientation

of an agent based on the sensor readings.

Our analysis, underscored by the classiﬁcations in Ta-

ble 16, indicates a strong preference for feature-based and

optimization-based SLAM approaches for visually impaired

navigation. This preference is likely due to the robustness and

efﬁciency of these methods in processing visual data, which

is key for real-time assistive navigation.

Figure 5 provides insight into the use of various localiza-

tion and mapping techniques for visually impaired navigation

from 2017 to the date when SLR was conducted (July 2023).

This ﬁgure illustrates that visual techniques has consistently

been used across all years. Although many other techniques

also operate based on visual data, we mentioned each of these

techniques, as indicated in the referenced studies.

The utilization of semantic SLAM and Cartographer

SLAM signiﬁes a recent trend towards leveraging advanced

spatial understanding and mapping capabilities for visually

impaired navigation. Semantic SLAM incorporates higher-

VOLUME 11, 2023 15

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 14. A summary of perspectives and innovations in SLAM-based navigation solutions, with insights into limitations and advantages - Part III.

Ref. Open-source Research Issue Contribution Advantages Limitations

[87] •Addressing the challenge of navi-

gation in unfamiliar indoor spaces

by designing a map-less system,

PathFinder

Developing a map-less navigation

robot system incorporating sign

recognition and intersection

detection, using scenario-based

participatory design with ﬁve blind

participants.

Enhanced navigation conﬁdence,

provided key sign information,

offered audio feedback, ensured

safe, independent mobility by

PathFinder

Limited study environments,

assumed environments without steps

or ﬂoor transitions, no empirical

comparison with participants’

regular aids, inability to recruit

younger participants and insufﬁcient

number of guide dog users, possible

positive bias from participants who

have previously participated in

studies, physical demand, Bluetooth

connectivity issues, privacy

concerns, inability to navigate

in congested spaces, surrounding

people may not recognize that

users are disabled as they don’t use

traditional aids.

[88] •Enhancing independent navigation

and social seat selection with a per-

ceptive robotic cane system.

Introducing a robotic cane with com-

puter vision for navigation and social

seat selection, featuring vibrotactile

feedback and successful pilot valida-

tion.

Independent navigation, social

norm-aware seat selection, intuitive

vibrotactile feedback, and effective

pilot-validated guidance.

Potential suboptimal chair detection,

discrepancy between user prefer-

ences and performance.

[89] •Overcoming limitations of existing

blind navigation methods by inte-

grating multi-sensory feedback for

comprehensive and intuitive mobil-

ity.

Integration of YOLO and ORB-

SLAM, enhanced by novel

algorithms, provides reliable

multi-sensory guidance

Enhanced accuracy, multi-sensory

feedback, real-time object detection,

dense navigation maps, and compre-

hensive guidance improve mobility

and safety.

Not explicitly mentioned

[90] •Addressing gap: aiding visually im-

paired in dynamic indoor navigation,

obstacle detection, and social dis-

tancing.

Wearable RGB-D assistant system

aiding indoor localization, mapping,

and dynamic obstacle detection.

Dynamic object detection, enhanced

obstacle avoidance, panoptic seg-

mentation for scene understanding,

robust tracking without additional

models, and RGB-D sensor integra-

tion.

Imperfect panoptic segmentation,

leading to errors in object

recognition; computational

complexity, inﬂuenced by the

number of dynamic objects present

in the scene.

[91] •Addressing the gap: wearable device

aiding visually impaired with indoor

object manipulation tasks.

Hand-worn assistive device, RGBD-

VIO method, effectivehuman-device

interface.

Enhanced pose estimation, depth

information utilization, improved

human-device interaction, effective

object manipulation.

Not mentioned

[92] •Addressing the challenge of indoor

navigation, integrating SLAM and

deep learning enhances environmen-

tal perception.

Introducing multi-functional smart

E-Glasses for enhanced indoor nav-

igation and lightweight object detec-

tion.

Real-time navigation, high object de-

tection precision, and robust SLAM

integration.

Future enhancements aim to improve

device comfort, portability, and size,

addressing user concerns and en-

hancing overall usability.

[93] •Addressing the gap in understanding

between visually impaired individu-

als’ perceptions and their actual sur-

roundings for navigation technology.

Introducing personalized navigation

system with object detection and de-

scription, and re-training models.

Personalized navigation, object de-

tection, reduced time for ﬁnding des-

tinations, and improved interaction.

Model re-training challenges, en-

vironmental complexity, and high

computational cost.

[95] •Addressing the need for a guiding

system to facilitate seamless walking

in public spaces.

Introducing a comprehensive system

aiding blind pedestrians by under-

standing nearby pedestrians’ behav-

ior.

Convenient suitcase design, accurate

motion tracking, effective tactile in-

terface for enhanced navigation.

Weight discomfort, space

constraints, speed adjustment

difﬁculties, and technological

improvements for enhanced

usability.

[96] •Addressing the need for advanced

navigation aid for the visually im-

paired using VSLAM technology.

Introducing a navigation aid sys-

tem merging VSLAM with pre-

established maps.

Not explicitly mentioned Not explicitly mentioned

[97] •Addressing the need for uniﬁed in-

door navigation, integrating scene

perception and visual localization.

Introducing a uniﬁed semantic visual

localization system, enhancing ob-

stacle avoidance and spatial aware-

ness.

Real-time awareness,

comprehensive understanding,

and obstacle avoidance.

Restricted camera ﬁeld of view and

inconsistencies in semantic segmen-

tation results impacting user conﬁ-

dence.

[98] Upon

publication

Addressing indoor navigation

challenges through smartphone-

based computer vision without new

infrastructure.

Real-time app development, robust

localization algorithm, and user-

friendly navigation.

Cost-effective deployment, enhances

usability, improves localization

accuracy, promises full-featured

wayﬁnding, and camera-agnostic

navigation for ease of use.

Challenges in wide, open indoor

spaces due to limitations in current

approach, suggesting potential for

augmented reality integration.

level scene interpretation and enhances users’ contextual

awareness. On the other hand, Cartographer SLAM provides

SLAM in 2D and 3D across various platforms and sensor

conﬁgurations, offering innovative solutions to tackle the

diverse challenges associated with BVI navigation.

ORB-SLAM algorithms, including ORB-SLAM (pub-

lished in 2015), ORB-SLAM2 (published in 2017), and ORB-

SLAM3 (published in 2021), have gained popularity because

of their robustness and performance. This can be attributed

to its efﬁcient feature extraction and matching techniques,

making it well suited for real-time navigation applications.

Customized techniques have been developed to meet spe-

ciﬁc needs. This trend indicates that researchers have adjusted

the SLAM techniques to better match the speciﬁc require-

ments of their intended applications. This suggests closer

integration of SLAM with domain-speciﬁc needs.

2) Sensor type

The sensors employed in SLAM solutions for BVI navigation

are diverse and include various types of cameras, LiDAR,

IMU, and other specialized sensors. As shown in Figure 6,

16 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 15. A summary of perspectives and innovations in SLAM-based navigation solutions, with insights into limitations and advantages - Part IV.

Ref. Open-source Research Issue Contribution Advantages Limitations

[99] •Addressing the indoor wayﬁnding

problem

Introducing specialized robotic nav-

igation aid, VIO method, guiding

modes, and Human Intent Detection

for enhanced navigation assistance.

Enhanced VIO method, two guiding

modes, and automated mode selec-

tion.

Validation in larger spaces, user

feedback incorporation, and opera-

tional restrictions.

[100] •Addressing independent indoor

corridor navigation through multi-

sensor fusion and semantic mapping.

Semantic mapping, multi-sensor

fusion, real-time performance

enhancement

Enhanced corridor navigation, se-

mantic mapping, landmark detec-

tion, real-time performance, multi-

sensor fusion for improved naviga-

tion experience.

Object recognition limitations, arrow

direction detection issue, training re-

quirement for unknown objects.

[101] SEVN-data 3Enhancing Reinforcement Learning

environments to develop a naviga-

tion assistant tailored for the BVI

community.

Developing a benchmark dataset and

Reinforcement Learning training en-

vironment to advance navigation

agent capabilities using real-world

imagery and neural architecture.

SEVN offers realistic training with a

rich, annotated dataset and a multi-

modal fusion model for effective

BVI navigation.

SEVN offers an extensible

Reinforcement Learning

environment, but improved model

performance requires.

[102] •Addressing real-time localization

and navigation in indoor settings

with a monocular camera, focusing

on computational efﬁciency, user-

friendly interfaces, and integrated

algorithms.

Introducing a non-ﬁlter based visual

SLAM with integrated object detec-

tion and distance-depth estimation

algorithms, using a single monocular

camera for BVI indoor navigation.

Utilizing a single camera and simpler

SLAM algorithm for cost-effective,

efﬁcient real-time performance.

Not mentioned

[103] •Enhancing indoor localization for vi-

sually impaired shoppers using text

extraction, addressing gaps in tradi-

tional visual assistance systems.

Expanding SLAM algorithm for

larger spaces using GIST/SURF

features and navigating through

text-rich environments.

Simplistic setup, no markers needed,

and efﬁcient real-time localization.

Operates only within length dimen-

sion of the aisle, limited by text den-

sity.

[104] Android APIs Enabling safe autonomous naviga-

tion for elderly and visually impaired

in crowded environments.

Design of an intelligent autonomous

scooter with advanced sensor fusion,

SLAM techniques, and hybrid map-

ping solutions.

Improved safety and autonomy, hy-

brid mapping for diverse environ-

ments, precise steering control.

Not mentioned

[105] •Localizing pedestrians in crosswalks

using visual odometry, addressing

challenges in uniform textures and

repetitive landmarks.

Introducing a prototype for localiz-

ing pedestrians in crosswalks using

visual odometry.

Accurate on weakly textured sur-

faces, addressing scaling issues in

monocular camera setups.

Initialization issues, tracking loss

due to strong orientation variations,

and challenges with oscillatory walk-

ing patterns.

[106] •Improving indoor pose estimation

accuracy for navigation aids using

plane features in feature-sparse envi-

ronments.

Introducing PAVIO method, utilizing

plane features and factor graph opti-

mization to improve pose estimation

for indoor navigation.

Improved pose estimation accuracy

and robustness, enhanced stability,

accurate 3D mapping.

Not mentioned

[107] •Addressing indoor navigation chal-

lenges to enhance mobility and inde-

pendence.

Implementation of SmartCane with

Google Tango for real-time indoor

navigation and demonstration of its

effectiveness.

Enhanced indoor navigation with

real-time path planning, multimodal

feedback, and an intuitive control

panel interface.

Requiring further user evaluation to

assess effectiveness

[108] •Developing a cloud and vision-based

system for safe navigation and de-

tailed perception.

Integrating cloud computing with

vision-based navigation, enhancing

perception, and improving object

recognition for blind individuals.

Detailed perception, real-

person safety support, abundant

surrounding information, and

improved object recognition.

Requiring extensive vision-based

mapping, struggles with similar

scenarios, and needs improved scene

parsing, currency validation, and

object recognition.

[109] •Addressing the need for specialized

navigation systems for visually im-

paired individuals using SLAM tech-

nology for real-time guidance.

Introducing a wearable camera with

LSD-SLAM for real-time position-

ing, obstacle detection, and route

guidance.

Efﬁcient calculation power, robust

performance, accurate mapping, dy-

namic adaptation, real-time assis-

tance.

Requires high-contrast environments

for accurate mapping; low contrast

may need external positioning solu-

tions.

[110] •Addressing the need for an innova-

tive navigation system using vision-

language model-based approach.

First BVI navigation system using

spoken instructions, visual-language

integration, and heuristic-based path

planning for improved success.

Running on portable devices, pro-

vides BVI navigation without heavy

labeling or 3D model reconstruction

in complex indoor environments.

Navigation reliability drops for long

distances.

[111] •Addressing real-time navigation la-

tency, safe route selection, and accu-

rate obstacle detection.

Integration of Web of Things, pre-

dictive analytics, YOLOv4 Tiny, and

SLAM for enhanced obstacle recog-

nition and navigation.

Enhanced obstacle recognition, nav-

igation, and safe route selection.

Requiring high-contrast

environments; needing external

updates when contrast is insufﬁcient

for accurate mapping.

[112] •Addressing the need for efﬁcient

navigation solutions for visually im-

paired individuals and patients with

lower body injuries indoors.

Developed a person carrier robot in-

tegrating Hector SLAM and Robust

Salient Detection for safe navigation

and obstacle avoidance.

Enhanced safety, improved mobility,

and effective object detection for in-

door navigation.

Sensitive to strong light, issues with

reﬂective surfaces, and occasional

collisions due to slow processing

speed.

3https://github.com/mweiss17/SEVN-data (Accessed on 12 May 2024)

we categorized the sensors into three types: cameras, LiDAR,

and other sensors.

Camera The common use of visual sensors in SLAM

techniques can be attributed to advances in computer vision

and image processing, which enhance navigation capabilities

by providing rich environmental information. This makes

visual-sensor-based SLAM techniques the most commonly

used in the implementation of assistive technologies for the

BVI people, offering a cost-effective, versatile, and accurate

solution for real-time navigation and spatial awareness. The

literature under review used the following types of cameras:

RGB, RGB-D, stereo, monocular, and other specialized cam-

eras.

RGB cameras are widely used due to their ability to capture

rich color information, which is beneﬁcial for visual odome-

try and object recognition. They are cost effective and widely

available, making them a popular choice for the development

of accessible navigation aids.

RGB-D cameras provide both color and depth informa-

tion, enabling more accurate mapping and localization. Depth

VOLUME 11, 2023 17

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 16. Localization and mapping approaches used for visually impaired navigation, with "NA" denoting data not available.

Widely applicable techniques

Technique Type Method Sensor type Reference(s)

Cartographer Scan matching Optimization-based LiDAR [59], [61], [87], [95]

Hector SLAM Scan matching Optimization-based LiDAR [69], [83], [112]

LiDAR SLAM Scan matching Rao-Blackwellized particle ﬁlter LiDAR [67], [68], [79]

FastSLAM [121] Feature-based Particle ﬁlter-based LiDAR [82]: mapping

Kinect Fusion Feature-based Optimization-based Visual [58]

Dynamic-SLAM Feature-based Optimization-based Visual [90]

LSD-SLAM Direct Optimization-based Visual [109], [111]

OpenVSLAM Feature-based Optimization-based Visual [60], [93], [97]

ORB-SLAM Feature-based Optimization-based Visual [64], [89]

ORB-SLAM2 Feature-based Optimization-based Visual [57], [62], [76], [92], [96], [101],

[82]: localization

ORB-SLAM3 Feature-based Optimization-based Visual [63], [65]

Pose-graph SLAM Feature-based Optimization-based Visual [77]

RTAB-Map Feature-based Optimization-based Visual [104]

Semantic SLAM Feature-based [66], [84] ,

NA [100]

Optimization-based [66], Particle

ﬁlter-based [84], NA [100]

Visual [66], [84], [100]

Visual SLAM Feature-based Optimization-based Visual [71], [72], [74], [85], [102], [108]

DSO Direct Optimization-based Visual [105]

VIO (Visual Inertial

Odometry)

Feature-based Optimization-based Visual [56], [98], [99]

Customized solution

DVIO Feature-based Optimization-based Visual [70]

PAVIO Feature-based Optimization-based Visual [78], [106]

RGBD-VIO Feature-based Optimization-based Visual [91]

VSLAMMPT Feature-based Optimization-based Visual [73]

Spatial tracking frameworks

Google ARCore Feature-based Likely optimization-based Visual [75]

ZED camera’s SLAM Likely feature-based Not explicitly stated Visual [103]

Apple iOS ARKit-based Likely feature-based Update process is implemented us-

ing a particle ﬁlter [80]

Visual [80], [110]

Intel RealSense SLAM Likely feature-based Not explicitly stated Visual [88]

Google Tango’s built-in

SLAM

Likely feature-based Not explicitly stated Visual [107]

information helps in understanding the 3D structure of the

environment.

Stereo cameras also provide depth perception through two

slightly offset lenses that simulate binocular vision. They are

effective in capturing detailed depth information and are use-

ful in applications where precise depth estimation is required.

Monocular cameras are simpler than stereo and RGB-D

cameras. They rely on visual odometry and other techniques

to estimate depth and motion, making them lightweight and

suitable for mobile applications.

Specialized cameras, including ﬁsheye, 3D time-of-ﬂight,

and wide-angle cameras, provide specialized capabilities,

such as a wide ﬁeld of view or precise depth measurement,

which can enhance the SLAM performance in speciﬁc sce-

narios.

LiDAR LiDAR sensors are highly accurate in measuring

distances and are effective in creating detailed 3D maps of

the environment. Studies use LiDAR alone to build a map of

the environment or in combination with other sensors such as

IMU and cameras to enhance the robustness and accuracy of

SLAM systems.

Other sensors Various studies combined different types of

sensors to leverage the strengths of each type and provide

more robust and reliable navigation solutions. For example,

integrating an IMU with a camera helps achieve better motion

tracking and stability. This trend towards integrating multiple

sensors highlights increasing efforts to enhance the robust-

ness and reliability of SLAM solutions.

The reviewed papers show a clear preference for RGB-D

cameras, indicating their effectiveness in providing both the

visual and depth information necessary for accurate SLAM

applications. The use of LiDAR is important in applications

that require precise mapping. Over the years, there has been

a noticeable trend towards integrating multiple sensors and

combining their strengths to achieve more robust and reli-

able SLAM solutions for visually impaired navigation. The

integration of machine learning-based techniques with SLAM

systems is particularly prevalent in solutions that utilize RGB-

D cameras. This highlights the effectiveness of combining

this type of data with advanced AI algorithms. This trend is

likely to continue as technology advances, offering more so-

phisticated and adaptable solutions in complex and dynamic

environments.

3) Computing resource

To process data and run localization and mapping algorithms,

the reviewed studies adopted two classes of computational re-

sources: local and remote. Local computations are performed

in situ on devices, such as smartphones, tablets, laptops,

portable microcontrollers, and UP board computers. In some

cases, algorithms were applied on PCs. Table 20 categorizes

the computing resources used in the reviewed studies for

localization and mapping tasks. Information regarding the

computing resources for the entire navigation assistive system

is detailed in Tables 35-37

18 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

FIGURE 5. Evolution and adoption of localization and mapping techniques in BVI navigation systems over time.

FIGURE 6. Overview of sensor types in studies under review

Local Computing Resources

Smartphones: Smartphone are widely used as commu-

nication gadgets, and their technology continues to grow

to the point that it is possible for smartphones to imple-

ment functional navigation systems. Because smartphones

integrate diverse sensors such as IMU, GPS, and cameras,

they can be used as a convenient tool for collecting environ-

mental information. In addition, their computational power

can be exploited to perform various navigation operations.

For example, the system proposed by [74] implemented all

algorithms relevant to data acquisition, ground segmentation,

moving direction search, global path planning, indoor and

outdoor localization, and object detection on a smartphone

and achieved real-time performance. Without an additional

depth sensor, [75] took advantage of an ARCore-supported

smartphone to track pose and to build a map of the surround-

ings in real time. However, despite the signiﬁcant advantages

of smartphones, such as their small size, low weight, easy

portability, and low cost, their computing power is not suf-

ﬁcient for some approaches.

Laptops and PCs: Some of the reviewed approaches per-

form all or part of the required calculations locally on a

portable computer, such as a laptop. Despite higher comput-

ing power compared to a smartphone, and greater security

compared to remote computational resources, the laptop’s

heavier weight and large size are considered major disadvan-

tages, especially during long trips. PCs provide even higher

computing power, which is essential for complex SLAM

operations; however, they lack portability.

Embedded systems and microcontrollers: Embedded

systems such as Nvidia Jetson boards, Raspberry Pi, and UP

boards provide a balance between computational power and

portability. They are commonly used in the reviewed studies

for performing SLAM operations locally. For instance, [58]

utilized a Hololens2 device with a GPU for real-time 3D

reconstruction, whereas [62] used a Jetson AGX Xavier for

ORB-SLAM2 and dense mapping. Raspberry Pi devices are

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Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 17. Comparison of core technical features for localization and mapping techniques, with a specific focus on sensor types, computing resources,

and whether machine learning-based methods are employed for localization and mapping tasks - Part I.

Ref. Sensor type Computing Resource Localization &Mapping Technique ML-Based Localization and Mapping

[56] Camera Smartphone Native AI library for iOS devices Not explicitly mentioned

[57] RGB-D camera Remote server ORB-SLAM2 •

[58] A depth, an RGB, and four gray scale

cameras, an IMU

Hololens2 device, GPU Iterative Closest Point (ICP) for camera

pose estimation, Kinect Fusion algorithm

for real-time 3D reconstruction

Not explicitly mentioned

[59] Wheel encoder and LiDAR Notebook PC Cartographer •

[60] RGB camera Cloud server and Nvidia Jetson AGX

Xavier

Visual place recognition, weighted aver-

aging, and perspective-n-point (PnP) for

localization, OpenVSLAM and Colmap

to generate a topometric map

NetVLAD for global descriptors and Su-

perPoint for local descriptors

[61] LiDAR Not mentioned Cartographer to build SLAM maps on

each ﬂoor

•

[62] RGB-D camera Jetson AGX Xavier ORB-SLAM2 for localization; down-

sampling, octomap, and 2D occupancy

grid mapping for an accurate dense map-

ping

•

[63] IMU, stereo and IR (depth) cameras Cloud server ORB-SLAM3 •

[64] RGB-D camera Raspberry Pi ORB-SLAM •

[65] Monocular RGB Not mentioned ORB-SLAM3 to obtain ground-truth

camera trajectory

•

[66] RGB-D camera High performance portable processor,

cloud server

Semantic visual SLAM based on ORB

feature to generate sparse, dense, and se-

mantic maps

ENet for pixel-level semantic segmenta-

tion

[67] LiDAR Raspberry Pi BreezySLAM •

[68] LiDAR Intel NUC computer GMapping for mapping and estimating

the destination’s location

•

[69] RGB-D camera Raspberry Pi3 B+ Hector SLAM for building the environ-

mental map and locating the user on the

map

•

[70] RGB-D Camera, IMU UP Board computer A VIO system developed based on

VINS-Mono

•

[71] RGB-D-IR camera A portable computer, Nvidia Jetson TX2 Hierarchical visual localization pipeline

with deep descriptors, geometric veriﬁ-

cation, and sequence matching.

NetVLAD and Dense Desc for advanced

descriptor extraction.

[72] RGB-D camera Odroid XU3 board, remote server Off-the-shelf algorithm [120] for 3D in-

door mapping; a two-stream ConvNet for

topological localization.

ConvNet replaces BoW for semantic

info; Inception-v3 enhances object

recognition, aiding localization.

[73] Two monocular cameras Not mentioned Followed a similar structure as ORB-

SLAM2.

•

[74] RGB-D camera, IMU A smartphone with Qualcomm Snap-

dragon 820 CPU 2.0 GHz

Vins-mono for indoor localization; ORB-

SLAM2 and Vins-mono for building a

indoor map repository.

•

[75] Smartphone’s camera A HUAWEI P20 smartphone with Kirin

970 CPU

Roberto Lopez Mendez ARCore SLAM

as the base for visual odometry and area

learning

Not explicitly mentioned

[76] Fisheye and depth camera An embedded CPU board ORB-SLAM2 for the building of the

virtual-blind-road (ofﬂine by a sighted

person) and the localization (online)

•

[77] SwissRanger SR4000 3D camera A Lenovo ThinkPad T430 laptop 2-step graph SLAM •

[78] SwissRanger SR4000 camera, IMU Up Board computer PAVIO: Fusing visual, inertial, and plane

features for robust SLAM localization

and mapping.

•

[79] Encoder, IMU, laser distance sensor Raspberry Pi 3 Model B and B+ Gmapping for building a 2D occupancy

grid map on the environment

•

[80] iPhone 11 Pro camera and IMU iPhone 11 Pro ARKit VIO for relative movements;

ARKit mapping for semantic labels;

Monte Carlo localization

YOLOv2 for object detection to facilitate

effective localization

also popular due to their low cost and sufﬁcient computing

power for many SLAM tasks [67], [69].

Remote Computing Resources

An alternative solution is to transfer all or part of the

calculations to the remote computing resources. To reduce

local computing costs, [57] adopted an embedded computer

and a remote server. In the proposed vision-based assistance

system, before transferring the input images to the server, the

images were time-stamped and encrypted on an embedded

computer. The remote server was equipped with a CPU and

GPU to run parallel ORB-SLAM2 and artiﬁcial intelligence

algorithms for indoor navigation, object detection, face recog-

nition, and scene text recognition. Experiments conﬁrmed

that the use of remote servers under a smooth network connec-

tion, such as 4G or WiFi, can meet the computational require-

ments of the proposed system. However, although the high

computing power of remote servers is considered a signiﬁcant

advantage, constant Internet access over a secure connection

is required. Moreover, the performance of the entire system

would be affected by the network condition.

B. RQ2. WHAT ARE THE ADVANTAGES AND LIMITATIONS

OF SLAM TECHNIQUES FOR BVI NAVIGATION?

20 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 18. Comparison of core technical features for localization and mapping techniques, with a specific focus on sensor types, computing resources,

and whether machine learning-based methods are employed for localization and mapping tasks - Part II.

Ref. Sensor type Computing Resource Localization &Mapping Technique ML-Based Localization and Mapping

[82] RGB-D camera, compass sensor Jetson Xavier AGX, NVidia FastSLAM for mapping, ORB-SLAM2

for local pose estimation in the pre-built

map

An extra thread aligns predicted seman-

tics with key features.

[83] LiDAR, ultrasonic sensor, Raspberry pi

camera

Raspberry Pi3 B+ Hector SLAM for constructing a 2D-map

of the environments and localization

•

[84] RGB-D camera, IMU Nvidia Jetson AGX Xavier microproces-

sor

RTAB-Map for semantic point cloud

generation and global localization

MobileNetV2 with PPM for constructing

semantic point cloud

[85] Monocular camera Nvidia Jetson Xavier NX Developer kit VisualSLAM foruser movementestima-

tion and stationary object localization

•

[87] LiDAR, IMU Nvidia RTX 3080 graphic board Cartographer for constructing a LiDAR

map

•

[88] RGB-D camera, IMU Dell G15 laptop with an RTX 3060 GPU SLAM implementation by RealSense for

creating an initial 2D occupancy grid and

estimating user pose

•

[89] RBG-D camera Uzel US-M5422 edge server, Raspberry

Pi 4B

Improved ORB-SLAM for generating a

dense navigation map and real-time po-

sitioning

•

[90] RGB-D camera Laptop Dynamic-SLAM based on ORB-SLAM2

for estimating user’s ego-pose and build-

ing a static feature point map

Non-Prior dynamic object detection pre-

ceding local mapping

[91] RGB-D camera, IMU Google Pixel 3 smartphone RGBD-VIO for mapping and accurately

estimating the device’s pose

•

[92] RealSense D435i camera Remote server based on Intel i7-8700

CPU, nvidia GTX1080 GPU

ORB-SLAM2 for environmental map-

ping and positioning of the user

•

[93] Google glasses camera GPU server OpenVSLAM for mapping and locating

user’s indoor positions

•

[95] LiDAR, IMU Laptop Cartographer for estimating the current

location and direction of a user

•

[96] RGB-D camera, IMU Not mentioned ORB-SLAM2 for mapping and localiz-

ing the user

•

[97] RGB-D camera Nvidia Jetson AGX Xavier processor OpenVSLAM forrobust mappingand lo-

calization in real time.

•

[98] iPhone 8’s IMU and rear-facing camera iPhone 8 Visual-Inertial Odometry with sign

recognition and geometric constraints.

Not explicitly mentioned

[99] RGB-D camera, IMU UP Board computer Visual-Inertial Odometry for 3D map-

ping and pose estimation

•

[100] RGB-D, LiDAR Laptop Semantic SLAM based on a 2D SLAM

technique for determining the corridor

area and mapping to a semantic map

YOLOv3 for landmark detection,

Places365 for place recognition

[101] Vuze+ camera Not mentioned ORB-SLAM2 for generating 3D posi-

tions and 2D connectivity graph from

Vuze+ footage

•

[102] Monocular camera Intel i7 processor Non-ﬁlter based visual SLAM using

trained objects as landmarks for localiza-

tion

ACF detector for object detection to iden-

tify trained objects of interest for local-

ization.

[103] ZED camera Nvidia Jetson TX2 ZED camera’s SLAM for navigating to-

ward an aisle in a grocery store.

Not mentioned

[104] Stereo camera, laser Nvidia Jetson TX2, Raspberry Pi, and

Arduino.

RTAB-MAP for obtaining ﬁne-grained

mapping of the 3D spatial world.

•

[105] Monocular camera Not mentioned Visual odometry for user localization

during street crossings

•

[106] 3D time-of-ﬂight camera , IMU UP Board computer Plane-aided visual-inertial odometry

(PAVIO) for pose estimation of an

robotic navigation aid

•

[107] Wide-angle lens camera, gyroscope, ac-

celerometers, and infrared sensor on

Google Tango.

Google Tango Google Tango’s built-in SLAM for map-

ping the environment and localizing the

user.

•

[108] Stereo camera Cloud server Visual SLAM for mapping the environ-

ment and localizing the user.

•

1) Advantages

Unlike many localization approaches, such as RFID- or GPS-

based methods, which require infrastructure setup, SLAM

does not depend on pre-existing infrastructure. It operates

autonomously by creating maps and understanding the sur-

roundings in real-time. SLAM relies on data captured by

sensors already present on many mobile devices, such as

smartphones, and offers a cost-effective solution for accurate

localization and mapping without the need for additional

hardware or subscription services.

One of the most important advantages of SLAM is its po-

tential for real-time positioning, which determines the agent’s

current location and orientation. Systems leveraging ORB-

SLAM, for instance, excel in pose estimation by integrat-

ing various data types, including visual, inertial, and depth

information, thus enhancing accuracy beyond conventional

methods [64]. This feature is pivotal not only for effective

navigation but also for obstacle detection and avoidance,

ensuring the safety and conﬁdence of visually impaired users

as they navigate through immediate environments [64], [89].

SLAM’s ability to reuse and update maps incrementally

allows for a high degree of environmental adaptation. Its

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Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 19. Comparison of core technical features for localization and mapping techniques, with a specific focus on sensor types, computing resources,

and whether machine learning-based methods are employed for localization and mapping tasks - Part III.

Ref. Sensor type Computing Resource Localization &Mapping Technique ML-Based Localization and Mapping

[109] Monocular camera A single CPU LSD-SLAM for estimating the user’s po-

sition and constructing a 3D environmen-

tal map

•

[110] Camera, IMU, LiDAR Smartphone visual-inertial SLAM based pose estima-

tion and 2D scene-graph map construc-

tion using iOS ARKit.

Not explicitly mentioned

[111] Kinect camera Not mentioned LSD-SLAM for assessing the user’s lo-

cation and building an environmental

map

•

[112] Laser Range Finder (LRF) sensor PC Hector SLAM for map building and

odometry calculation

•

TABLE 20. Categorization of computing resources used for localization and mapping tasks.

Computing resource Description References

Remote/Cloud servers Used for high-computation tasks, leveraging the power of remote resources. [57], [60], [63], [66], [72], [92], [93], [108]

Smartphones/Tablets Common in applications prioritizing portability and accessibility. [56], [74], [75], [80], [91], [98], [107], [110]

Nvidia Jetson For balancing computational power and portability. [60], [62], [71], [82], [84], [85], [87], [97], [103], [104]

Raspberry Pi Chosen for its affordability and sufﬁcient power. [64], [67], [69], [79], [83], [89], [104]

Laptops and PCs Employed for tasks needing robust computational capabilities and ﬂexibil-

ity in hardware.

[59], [77], [88], [90], [95], [100], [112]

UP Board computer Used for handling intensive computationtasks while maintaining a compact

form factor.

[70], [78], [99], [106]

Other speciﬁc systems Includes a variety of speciﬁc embedded solutions tailored to the require-

ments of each study.

[58], [68], [72], [76], [89], [102], [104], [109]

capacity to relocalize within prebuilt maps or expand them as

necessary ensures that users can rely on updated information

for navigation [75], [76]. This adaptability is further enhanced

by the capability of the system to handle dynamic environ-

ments, making it invaluable for visually impaired users who

require real-time path adjustments in response to moving

obstacles [73], [90].

Detailed environmental mapping facilitated by SLAM,

ranging from two-dimensional layouts to complex 3D geo-

metric and semantic maps, provides comprehensive spatial

understanding [59], [67], [72]. Environmental awareness is

critical for path planning and collision avoidance. Further-

more, the integration of semantic mapping enriches spatial

understanding by adding contextually rich information to

maps, thereby facilitating more informed decision-making

and interaction with the environment [66], [100].

The integration of different types of sensors and technolo-

gies with SLAM signiﬁcantly expands the scope of its appli-

cation. By integrating techniques such as object detection al-

gorithms or combining RGB-D and IMU sensor data, SLAM

systems achieve a multilayered perception of the environment

[64], [84]. Sensor fusion enhances a system’s ability to detect

and classify objects, accurately navigate, and handle dynamic

elements within the environment, thereby offering a more

holistic assistive solution [83].

The cost-effectiveness of SLAM-based solutions attributed

to their reliance on widely available low-cost sensors makes

this technology particularly interesting. Systems employing

monocular cameras or wearable RGB-D cameras exemplify

how SLAM can be implemented in a cost-effective manner

without compromising functionality, thus making advanced

navigational aids accessible to more users [69], [85]. The

advantages of SLAM, derived from the literature, are listed

in Table 21.

2) Limitations

Although SLAM technologies show great potential for im-

proving navigation aids for the visually impaired, they are not

without their limitations. These limitations can signiﬁcantly

impact the effectiveness and reliability of SLAM-based assis-

tive systems.

A notable challenge is the computational complexity and

the associated demand for system resources. The imple-

mentation of advanced SLAM algorithms and the integra-

tion of deep learning frameworks for semantic understand-

ing introduce signiﬁcant computational overhead [57], [72].

This complexity can compromise the real-time performance,

which is crucial for assistive navigation. The need for appro-

priate hardware to process high-resolution data further under-

scores this limitation, potentially restricting the deployment

of SLAM-based systems [89].

The effectiveness of SLAM is dependent on its environ-

mental characteristics. Accurate mapping and localization

depend on the presence of distinct geometric features. In

environments lacking such features or dynamically changing

settings, SLAM systems may struggle to maintain accurate

localization, thereby leading to navigation errors [77]. This

limitation is particularly evident in feature-poor areas such as

long corridors or spaces with uniform surfaces, where loss of

localization can occur [62].

Another critical limitation is the dependence on initial data

or pre-existing maps. Some SLAM systems require sighted

individuals to pre-map the environment, which can limit the

ﬂexibility and immediate usability of unmapped or altered

spaces [75], [76]. This reliance on prior mapping can be a

signiﬁcant hurdle for deploying SLAM-based navigation aids

22 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 21. Key features of SLAM that benefit visually impaired navigation.

Advantages Description Ref.

Accurate localization SLAM provides precise positioning within an envi-

ronment, essential for effective navigation and obsta-

cle avoidance.

[56]–[58], [62], [64], [67]–[70], [73]–[78], [82],

[83], [85], [88]–[93], [95], [97]–[99], [102], [106]–

[109]

Environmental mapping SLAM constructs maps of surroundings, enabling

spatial awareness for navigation.

[57]–[61], [64], [67], [69], [70], [72]–[77], [79], [83],

[84], [87]–[90], [92], [93], [96], [97], [99], [100],

[104], [107]–[112]

Map reuse Previously created maps can be reused to enhance the

efﬁciency and reduce the need for constant remap-

ping.

[57], [75], [76], [93], [109]

Loop closing SLAM corrects trajectory drifts by recognizing previ-

ously visited locations, thereby improving long-term

accuracy.

[57], [63], [84]

Semantic mapping SLAM integrates contextual information into maps,

thereby enhancing the understanding of the environ-

ment and its elements.

[66], [84], [100]

Object localization SLAM identiﬁes and positions objects within the

environment, facilitating interaction and navigation

around the obstacles.

[72], [85], [88], [91]

Dense navigation maps SLAM generates detailed maps that provide rich en-

vironmental data that are crucial for complex naviga-

tion tasks.

[64], [66], [89]

Incremental map updating SLAM continuously updates maps with new informa-

tion, ensuring that they remain accurate and current.

[64], [69], [89], [109], [111]

Integration with other technologies SLAM can be combined with other technologies and

algorithms to enhance functionality and performance.

[64], [68], [72], [83]–[85], [89], [92], [98], [102]

Integration of sensors SLAM utilizes a variety of sensors to enrich the

environmental perception and mapping accuracy.

[58], [84], [91], [99], [100], [104]

Cost-effectiveness and accessibility SLAM’s reliance on commonly available sensors

makes it an affordable solution for widespread use.

[57], [60], [66], [67], [69], [73]–[76], [80], [83], [85],

[98], [102]

Ground-truth trajectory SLAM delivers accurate path tracking, aiding the

development of reliable navigation instructions.

[63], [65]

Integration with 2D map SLAM data can be integrated with 2D maps to en-

hance the navigation accuracy and functionality.

[63], [70], [75], [77], [80], [98]–[100]

Dynamic environment handling SLAM can be adapted to changes within environ-

ment, maintaining reliable navigation in the presence

of moving obstacles.

[73], [90], [100], [104], [109], [111]

Re-localization SLAM can quickly regain accurate positioning after

temporary tracking loss, ensuring continuous and re-

liable navigation.

[57]

in diverse and changing environments [82], [96].

Drifting errors present a substantial challenge for main-

taining the long-term accuracy of SLAM systems. Over time,

small inaccuracies can accumulate, leading to signiﬁcant de-

viations from the true trajectory, which can disorient users

and compromise the navigation safety [56]. In addition, the

ineffectiveness of some SLAM systems for generating dense

navigation maps limits their utility in providing the detailed

guidance required for visually impaired navigation, necessi-

tating further algorithmic enhancements [64].

The performance of SLAM in dynamic environments,

characterized by moving obstacles and changing conditions,

remains a critical concern. Systems may fail to adapt quickly

to such changes, leading to potential navigation errors and

safety risks for visually impaired users [68].

Some SLAM applications require external calibration or

setup, such as placement of calibration boards in speciﬁc

environments. This requirement can limit the spontaneity

and ease of use of SLAM-based navigation aids because it

imposes additional constraints [105]. Table 22 outlines the

overall limitations of SLAM, derived from the publications

under review.

C. RQ3. WHAT CHALLENGING SITUATIONS HAVE BEEN

ADDRESSED?

This section explores various challenging situations ad-

dressed by SLAM-based navigation-assistive systems for

BVI individuals. We categorized these challenges into two

main groups: those relevant to environmental complexities

and those related to the sensors used for receiving environ-

mental data. Additionally, we discuss practical challenges and

considerations that impact the usability and adoption of these

systems.

1) Technical and methodological challenges

Optimal pathﬁnding, perception of surroundings, and ob-

stacle avoidance are crucial for navigation. Precise local-

ization of a visually impaired user within the environment

is essential for the effective operation of these functions.

Our surveyed papers addressed the localization and mapping

problems using various techniques. Some of these studies

explored other navigation-related challenges. We categorized

these challenges into two groups: those relevant to environ-

mental complexities, and those related to the sensors used to

receive environmental data. Dynamic obstacles and crowded

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Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 22. Limitations of SLAM for visually impaired navigation.

Limitations Description Ref.

Complexity and computational

requirements

SLAM’s advanced algorithms can demand signiﬁcant computational power, impacting real-time

performance and efﬁciency.

[57], [72], [89], [90]

Dependency on environmental

features

The accuracy of SLAM depends on the presence of distinct environmental features, which limits

its effectiveness in feature-poor or dynamically changing environments.

[77], [109], [111]

Dependence on initial

data/prior maps

Some SLAM systems require pre-mapped environments or initial data setups by sighted individu-

als, thereby reducing the ﬂexibility in unmapped or altered spaces.

[75], [76], [82], [84],

[93], [96], [109], [112]

Loss of localization in feature-

poor areas

SLAM may experience frequent localization losses in areas lacking sufﬁcient feature points, such

as blank corridors or plain walls, thereby compromising navigation reliability.

[62]

Dependency on external cali-

bration

The need for external calibration in certain SLAM applications can limit their spontaneity and

practicality in unprepared environments.

[105]

Drifting error Accumulating drifting errors in SLAM can reduce the long-term accuracy, leading to potential

navigation inaccuracies and user disorientation.

[56]

Dense maps fail to align with

real-world conditions

The inability of certain SLAM systems to generate detailed dense maps can restrict their effective-

ness in providing a comprehensive navigation guidance.

[64]

Vulnerability in dynamic envi-

ronments

SLAM systems may struggle to adapt to dynamic environments with moving obstacles, thereby

posing navigation challenges and safety risks.

[68]

TABLE 23. Challenges addressed in reviewed studies using SLAM

techniques

Related to Challenges Reference(s)

Environment Crowded places [59], [65], [68], [95], [104]

Dynamic object [59], [68], [71], [73], [90], [100],

[109], [111]

Sensor Fast motion [72]

Illumination [61], [71], [72], [74], [81]

spaces constitute the challenges in the ﬁrst group, whereas

challenges related to changing lighting conditions and the

rapid motion of users that results in motion blur fall into

the second group. Table 23 lists studies that investigated

these challenges through the integration of SLAM with other

approaches. In the following section, we discuss the studies

that address these challenges.

a: Environmental complexities

Crowded scenarios Navigating crowded environments

presents signiﬁcant challenges for the visually impaired, lead-

ing to increased collision risks and difﬁculties in maintaining

personal spaces. The absence of visual information makes

it difﬁcult to measure distance, perceive crowd density, and

locate landmarks or places of interest.

Navigation in crowded environments also poses challenges

for assistive technologies. For example, in assistive systems

that operate based on SLAM, the presence of numerous

dynamic elements, such as moving individuals and objects,

introduces ambiguity into feature detection and tracking,

leading to difﬁculties in accurately estimating the pose of

the user and structure of the environment. The dynamic na-

ture of crowds also hinders loop closure detection, disrupts

map consistency, and contributes to drift. Moreover, the lack

of distinct visual landmarks in crowded scenes represents

a reliable localization challenge, which potentially reduces

the robustness and accuracy of the overall SLAM system.

Addressing these challenges requires the development of new

approaches to address the complexity of such environments

effectively. Several studies have investigated this issue.

[59] presented a guide mobile robot engineered for the

complexities of navigating different environments while con-

sidering dynamic objects and human presence. The robot

could handle crowded environments with multiple dynamic

objects. To accomplish this, the robot leveraged a spatial risk

map, which is a tool that evaluates potential object-occupied

spaces, to chart a path that effectively minimizes disruptions.

This study presents experiments in which a robot successfully

guided a user through the passage of multiple objects and

people.

In another study, [65] introduced an egocentric human

trajectory forecasting model that was designed for navigation

in crowded environments. By leveraging a wearable camera,

the model captures detailed surroundings and the user’s past

trajectory to predict their future. The trajectories obtained

using ORB-SLAM3 were used to train the egocentric human

trajectory forecasting model, which predicted the wearer’s

future trajectory based on contextual cues captured by the

wearable camera and the wearer’s past trajectory.

In addition, [68] addressed challenges in crowded environ-

ments using a combination of SLAM and Ultra-Wideband

(UWB) positioning. However, the SLAM algorithm was

found to be less effective in environments with dynamic

obstacles such as pedestrians. The algorithm ﬁnds features

of dynamic obstacles moving along with the robot as the

assistive device, and thus, it was misled that the robot did not

move at all. However, using UWB positioning mitigates this

issue.

In [95], the method addressed crowded environments by

recognizing and predicting people’s behavior while anticipat-

ing the collision risk. The system advises users to adjust their

walking speed (on-path mode) or to choose alternative routes

(off-path mode). This involves comparing the 3D point cloud

map to real-time LiDAR and IMU sensor data. The system

then predicts the future position and velocity of the user in

order to avoid collisions.

An intelligent autonomous scooter was developed in [104]

for navigating environments with small safety margins and

highly dynamic pedestrian trafﬁc such as sidewalks with

24 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

numerous obstacles and pedestrians. The authors proposed

a hybrid mapping solution that combines far-ﬁeld and near-

ﬁeld mapping to navigate through dynamic environments.

This approach utilizes sensor fusion to adapt dynamically to

complex and cluttered environments.

Dynamic objects Several publications have not directly

addressed the challenge of crowded environments, and have

only focused on dealing with the presence of dynamic objects

within the scene. In the system proposed by [90], dynamic

objects can be identiﬁed, and average depth information can

be provided to the user. When a dynamic object belongs to a

predeﬁned class such as a person, it can also be tracked be-

tween frames in the SLAM pipeline. The system is capable of

identifying and tracking dynamic objects after ego-motion es-

timation to obtain average depth information. Subsequently,

it can estimate the poses and speeds of these tracked dynamic

objects and relay this information to the users through acous-

tic feedback. Depth information helps users maintain social

distancing in public indoor environments such as shopping

malls.

To address the challenge of dynamic objects, [73] proposed

a new method called visual simultaneous localization and

mapping for moving person tracking (VSLAMMPT). This

method was designed to handle dynamic environments in

which objects constantly move. The system also uses ex-

pected error reduction with active-semi-supervised learning

(EER–ASSL)-based person detection to eliminate noisy sam-

ples in dynamic environments. This aids in accurate detection

and avoidance of dynamic obstacles.

[100] utilized YOLOv3 to detect common objects in a

corridor, including people, which were identiﬁed as obstacles.

The system sends information about obstacles to users every

ﬁve seconds when the distance between the user and obstacle

is less than 10 m. For example, it may notify the user, "A

person is located 2.8 meters ahead."

b: Sensor-related challenges

Changes in lighting condition Lighting changes pose a

hurdle to visual SLAM systems. Illumination variations al-

ter the visual features, interfere with accurate detection and

matching across frames, impact pose estimation, and map-

building robustness. SLAM relies on distinctive features for

operation; however, lighting changes introduce ambiguities,

noise, and errors, which affect the accuracy. Overcoming this

challenge requires robust algorithms for dynamic lighting to

ensure stable and precise localization and mapping.

The method proposed in [71] tackles the challenge of

illumination changes using a deep descriptor network called a

Dual Desc, which is designed to be robust against various ap-

pearance variations including illuminance changes. The net-

work used multimodal images (RGB, Infrared, and Depth) to

generate robust attentive global descriptors and local features.

These descriptors were used to retrieve coarse candidates

from query images, and 2D local features, along with a 3D

sparse point cloud, were used for geometric veriﬁcation to

select the optimal results from the retrieved candidates. The

authors mentioned that their dataset included images captured

at different times of the day, which resulted in illumination

changes between the query and database images. Despite

these changes, the proposed method achieved satisfactory

localization results.

The authors of [72] evaluated the inﬂuence of lighting con-

ditions on the performance of their novel localization method.

The authors captured training images during the day and test

images at night and simulated changes in lighting conditions

by switching some of the lights off in locations without

windows. The results showed that changes in the lighting

conditions had a minor impact on the proposed method.

[61] mentioned that the proposed localization scheme was

veriﬁed in a typical ofﬁce building environment with dra-

matically changing lighting conditions throughout the day;

however, it does not provide detailed results or discussion on

how changing lighting conditions affect the performance of

the system.

The method proposed by [81] addressed changes in illu-

mination as a challenge using the COLD and IDOL datasets,

which were recorded under different weather and illumination

conditions (cloudy, night, and sunny) using different mobile

platforms and camera setups. These datasets were used to

evaluate the strength of the localization and recognition al-

gorithms with respect to the variations caused by human ac-

tivities and changes in illumination conditions. The study also

mentioned the use of Histogram of Oriented Gradients (HOG)

for feature extraction, which provides preferable invariant

results for lighting and shadowing.

Motion blur Blurred images in visual SLAM can lead to

inaccuracies in feature detection and matching, causing issues

with pose estimation, map building, loop closure, and visual

odometry. These inaccuracies can also impact depth measure-

ments and map quality. To address this issue, strategies such

as using high-frame-rate sensors, incorporating IMUs, and

employing motion-deblurring techniques can be employed to

improve the accuracy of localization and mapping in SLAM

systems.

Motion blur can be caused by fast or sudden movements

of the user during navigation, which can affect localization

performance. [72] studied this challenge and evaluated the

robustness of the localization methods. They captured 2316

blurred images on the testing day. The results show that the

proposed method performed poorly in this experiment, indi-

cating that fast motion or sudden changes in user movement

can pose a challenge to the system. The reason for this poor

performance is that the object detection scores did not exceed

the threshold during the experiment.

2) Practical challenges and considerations

In addition to the technical and methodological aspects, we

recognized the importance of practical challenges considera-

tions that can signiﬁcantly affect the usability and adoption

of SLAM-based assistive systems. Therefore, we included an

evaluation of the practical challenges and operational efﬁ-

ciency, as summarized in Tables 24 and 25. The information

VOLUME 11, 2023 25

Bamdad et al.: SLAM for Visually Impaired People: a Survey

in these tables has either been directly extracted from the ar-

ticle or can be easily inferred from the article’s text. These ta-

bles provide information on user-friendliness, cost-efﬁciency,

weight, comfort for extended use, adjustable ﬁt, fatigue mit-

igation, and portability of the assistive tools described in

the reviewed studies. For instance, while smartphones and

lightweight devices such as eyeglasses-mounted sensors [74]

and ARCore-supported smartphones with haptic gloves [75]

are generally well received because of their high portability

and ease of use, heavier devices such as guiding robots [68]

and rolling suitcase-shaped device [95] are noted to cause

user fatigue over extended periods. The augmented cane

[67], although found to improve conﬁdence and workload for

novice and expert users, also faced usability challenges owing

to its weight. Clear instructions and easy learning curves,

as seen in electronic glasses with haptic modules [57], play

a signiﬁcant role in enhancing user satisfaction. However,

the cost efﬁciency of these technologies varies, with some

solutions being more affordable and accessible.

D. RQ4. HOW THE PROPOSED SOLUTION IS EXPECTED

TO ENHANCE MOBILITY AND NAVIGATION FOR VISUALLY

IMPAIRED?

This section discusses how the approaches proposed by the

studies included in our SLR have the potential to improve

navigation for BVI people. These studies focused on diverse

attributes, such as accurate pose estimation, semantic map-

ping, sensor fusion, and algorithmic innovations, to improve

the quality of BVI navigation. Table 26 presents the catego-

rization of attributes that contribute to enhancing the mobility

and navigation of visually impaired individuals.

To understand the impact of these solutions further, we

examined their effectiveness in real-world scenarios. Various

localization and mapping techniques have been assessed on

the basis of their accuracy, robustness, consideration of dy-

namic objects, and running time. This evaluation provides

insight into the performance of these techniques in practical

environments.

In addition, we considered user-based evaluations to gauge

user satisfaction and the practical applicability of the pro-

posed system. These evaluations include feedback from ac-

tual users, which is crucial for understanding real-world us-

ability and acceptance of assistive technologies.

Furthermore, we provide a detailed overview of the compo-

nents and technologies used in assistive navigation systems.

This helps to understand the practical implementations and

innovations proposed by the studies. By examining the system

prototypes, we gained insights into the design and func-

tionality of assistive solutions beyond the localization and

mapping components. This offers a comprehensive view of

how these technologies enhance the mobility and navigation

of the visually impaired.

1) Attributes enhancing mobility and navigation

SLAM technology is primarily used to provide precise local-

ization, which is critical for assistive navigation systems. Pre-

cise localization provides accurate information regarding a

user’s position in the environment in which the user navigates.

This accuracy enables the system to offer feedback on obsta-

cles, pathways, and points of interest, thereby allowing BVI to

navigate safely and conﬁdently. Real-time assistance has also

emerged as the key feature. Providing immediate feedback

on the environment enables users to travel efﬁciently and

safely, which leads to increased mobility and independence.

Semantic mapping generates maps beyond geometric data.

Such representations contain not only spatial information but

also the semantic meanings of objects and features within

the environment. This semantic understanding offers a deeper

insight into the environment. This contextual awareness is

particularly beneﬁcial for enhancing navigation accuracy, as

it enables navigation systems to make decisions based on se-

mantic context, improving obstacle avoidance, path planning,

and overall navigation efﬁciency.

By employing robotic systems such as small robots,

smart canes, and sensor-equipped suitcases, some studies

have provided guidance, obstacle avoidance capabilities, and

increased spatial awareness, thereby effectively providing

independent navigation for visually impaired individuals.

Smartphone-based solutions harness the ubiquitous nature of

smartphones that are equipped with cameras and sensors.

These solutions offer navigation assistance by using widely

available and familiar devices. Both indoor and outdoor nav-

igation capabilities offer a seamless transition between en-

vironments, ensuring that users receive consistent support,

regardless of the scene in which they navigate. Innovative

localization and mapping algorithms enhance navigation ef-

ﬁciency and effectiveness through tailored modiﬁcations of

existing SLAM frameworks or through the creation of novel

solutions. Ultimately, these advancements have led to an im-

proved overall experience for individuals with visual impair-

ment. Although these studies focused on different features

and attributes, they all aimed to enhance mobility, indepen-

dence, and overall quality of life for BVI people.

2) Effectiveness of localization and mapping techniques in

real-world scenarios

The effectiveness of the localization and mapping techniques

in real-world scenarios varies across studies. Tables 27-29

summarize these evaluations, highlighting key attributes such

as the working area, localization and mapping accuracy level,

robustness level, consideration of dynamic objects, and run-

ning time. The robustness and accuracy levels reported in

these tables are extracted from each paper’s context; each

rating reﬂects conditions speciﬁc to that paper and is not

necessarily superior or inferior to the other approaches. Thus,

these values are not comparable due to differing conditions

across the papers.

Many studies, such as [57], [59], and [62], have demon-

strated high localization and mapping accuracy, particularly

in indoor environments. These studies employed techniques

such as ORB-SLAM2, OpenVSLAM, and Cartographer to

ensure reliable feature matching and adaptive navigation.

26 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 24. Practical challenges and operational efficiency - Part I.

Ref. Assistive tool User-friendliness Cost-

efﬁcient

Weight Comfort

for

extended

use

Adjustable

ﬁt

Fatigue

mitigation

Portability

Clear

instructions

Easy to learn

[56] Smartphone ✓ ✓

[57] Electronic glasses

and leg-mounted

haptic modules

✓ ✓ Light ✓ ✓ ✓ High

[58] Microsoft Hololens2 ✓Light ✓High

[59] Wheeled guide mo-

bile robot

✓Light ✓Easy-to-

hold handle

Moderate

[60] Android application ✓ ✓ Light ✓ ✓ ✓ High

[61] Not a user-based evaluation: only a technical test.

[62] Helmet; white cane ✓Light ✓Moderate

[63] Smartphone ✓Need time to

learn interpreting

tactile signals

[64] Smart cane ✓Light High

[65] No prototype implemented; the proposed approach for trajectory forecasting was tested with a robot.

[66] A forehead-mounted

camera, an earphone,

a computing resource

bag

Not a user-based evaluation: only a technical test performed. ✓Heavy Heavyweight Heavyweight Moderate

[67] Augmented cane ✓ ✓ ✓ Heavy

(1kg)

Heavyweight ✓Heavyweight Moderate

[68] Guiding robot ✓ ✓ USD 17000 Heavy

(25kg)

Comfortable

handle

feedback;

some users

noted

slewing

and speed

change

discomfort.

The robot

is relatively

large at

41x43x25

cm3.

[69] Head-mounted cam-

era

Not a user-based evaluation: only a technical test performed. ✓Light

[70] Smart cane Not a user-based evaluation: only a technical test performed.

[71] Auxiliary glasses Not a user-based evaluation: only a technical test performed.

[72] No prototype implemented: Only a technical test performed.

[73] Smart eyeglasses Limited

instructions

✓High

[74] Eyeglasses-mounted

sensors + smartphone

✓Participants

trained in 10

minutes

✓Light Prolonged

beeping

may cause

discomfort.

Glasses

weight

strains the

nose.

High

[75] ARCore-supported

smartphone + haptic

gloves

✓A 5-minute

tutorial

✓High

[76] Optical see-through

glasses

✓High

[77] Smart cane Heavy Low

comfort

Heavyweight Bulky

tablet hangs

on neck,

heavy cane

[78] Computer-vision-

enhanced white cane

Not a user-based evaluation: only a technical test performed.

[79] Smart robot No information available.

[80] Smartphone ✓✓✓✓✓High

Robustness is another critical factor, with many systems

proving to be resilient under various conditions. Studies such

as [61] and [71] reported high robustness owing to the inte-

gration of multiple sensors and multi-modal imaging. These

systems can navigate complex environments and maintain

accurate localization.

However, some studies highlighted some challenges. For

instance, [68] indicated that SLAM-based systems struggle

with dynamic environments, leading to unstable navigation

and orientation errors. Similarly, [82]pointed out issues with

SLAM-relative poses in changing or occluded feature scenar-

ios that affect navigation stability.

The running time is another essential consideration, with

many studies emphasizing real-time performance. Systems

such as those described in [66] and [67] provide real-time per-

formance, which is crucial for assistive navigation. However,

some systems, such as those in [72], face longer computa-

tional times owing to their increased complexity, which can

be a drawback in real-world applications.

Overall, the evaluation of localization and mapping tech-

niques across different studies revealed a range of perfor-

mance levels. High accuracy and robustness are common

in controlled indoor environments, whereas dynamic and

complex scenarios pose signiﬁcant challenges. Insights from

VOLUME 11, 2023 27

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 25. Practical challenges and operational efficiency - Part II.

Ref. Assistive tool User-friendliness Cost-

efﬁcient

Weight Comfort

for

extended

use

Adjustable

ﬁt

Fatigue

mitigation

Portability

Clear

instructions

Easy to learn

[82] Head-worn camera Easily

understood

commands

10-minute

training

Commercially

available

hardware

Heavy

(5.5lbs ≈

2.49kg)

High

[83] Sensors attached to a

white cane

Not a user-based evaluation. ✓Not a user-based evaluation: only a technical test performed.

[84] No prototype implemented; the proposed approach for real-time global localization was tested with an agent.

[85] Chest-mounted cam-

era

Not a user-based evaluation ✓Light Not explicitly mentioned. High

[87] Suitcase-shaped

robot

Improved with

intuitive

terminology

Positive usability

scores

-Heavy

(40lbs ≈

18.14 kg)

Physical

demand

- Heavyweight Bulky and

heavy

[88] Robotic cane

[89] Smart cane The focus of the paper is primarily on the technical aspects of the multi-sensory blind guidance system.

[90] Smart glasses Light

[91] Hand-worn device Not a user-based evaluation: only a technical test performed. 52.5 grams

[92] Smart E-glasses Areas for

improve-

ment

[93] Google glasses Information not provided; Only a technical test conducted with a laptop PC as a navigator.

[95] A rolling suitcase-

shaped device

✓Short training

session (10-20

minutes)

Heavy Weight dis-

comfort

Heavy and

bulky

Space limi-

tations

[96] System testing was not feasible as the system was in the experimental stage.

[97] Smart glasses Not a user-based evaluation: only a technical test performed.

[98] Smartphone ✓ ✓ ✓ Light ✓ ✓ ✓ High

[99] White-cane mounted

camera

Information not provided; Only a technical test performed.

[100] TurtleBot2 robot

(to be replaced by

portable device) +

wearable sensors

Heavy Heavy and

bulky

Bulky

[101] Paper develops a Reinforcement Learning environment to create a navigation assistant tailored for the BVI community, without user-based evaluation.

[102] Smartphone ✓Light ✓ ✓ ✓ High

[103] No prototype implemented; the proposed approach was tested within a research building.

[104] Intelligent

autonomous scooter

High cost Heavy Bulky

[105] Wearable camera Light High

[106] Computer-vision-

enhanced white cane

Not a user-based evaluation: only a technical test performed.

[107] Smart cane + Google

Tango

225 grams High:

light and

compact,

usable in

multi-ﬂoor

[108] Helmet-mounted

camera + android-

based smartphone

Not a user-based evaluation: only a technical test performed.

[109] Wearable camera Not a user-based evaluation: only a technical test performed.

[110] iPhone 12 Pro Max Effective

language

instructions

Intuitive

navigation

process

Light - High

[111] Wearable camera Not a user-based evaluation: only a technical test performed.

[112] Person carrier robot

(wheelchair)

Heavy Bulky

these evaluations are crucial for understanding the practical

applicability and limitations of SLAM-based assistive sys-

tems for visually impaired individuals.

3) User-based evaluations

This section analyzes the user-based evaluations conducted to

assess the satisfaction of the proposed SLAM-based assistive

systems. By examining these evaluations, we gained insights

into the real-world applicability and user acceptance of these

technologies. Several studies conducted user-based evalua-

tions with actual participants to assess the effectiveness of and

satisfaction with their proposed systems. These evaluations

provided valuable insights into the usability and acceptance

of assistive technologies. Tables 30 and 31 summarize studies

that include user-based evaluations.

Three methods for assessing user satisfaction were iden-

tiﬁed: user studies, interviews, and surveys. Additionally,

some studies involved only visually impaired participants,

some involved only blindfolded users, and some included

both groups to test their systems. Most studies used user

studies as the primary evaluation method. Some studies also

employed interviews or surveys after initial user studies to

gather additional information on user satisfaction. The tables

also show the experimental sites where the evaluations were

28 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 26. Attributes of SLAM-based navigation systems that contribute to enhancing BVI navigation, along with referenced papers emphasizing

each feature.

Features Description Reference(s)

Precise Localization Using SLAM algorithms, these solutions accurately estimate the position and orientation of visually

impaired users. Precise localization is essential for visually impaired navigation systems in order to

ensure accurate real-time guidance, obstacle avoidance, and spatial awareness, ultimately enhancing

independent and safe mobility.

[64], [67], [69], [70],

[73], [75]–[78], [82],

[89], [90], [93], [95],

[97], [102]

Real-time assistance This feature ensures that BVI users receive immediate feedback about their environment. Therefore,

they can be guided to navigate safely and efﬁciently, thereby enhancing their overall mobility and

independence.

[57], [66], [67], [69],

[77], [82], [85], [88],

[92]

Semantic mapping Semantic mapping involves creating detailed environmental representations that go beyond geometric

data and enables visually impaired users to navigate with a deeper understanding of their surroundings.

[66], [72], [84], [97],

[100]

Both indoor and outdoor

navigation

Systems that serve both indoor and outdoor settings allow users to transition seamlessly between

different environments while receiving consistent support.

[56], [65], [67], [68],

[74], [85], [104], [108],

[111]

Innovative algorithms Innovative algorithms lead to advancements in navigation techniques. These approaches contribute to

a more efﬁcient and effective navigation, ultimately improving the overall experience of the visually

impaired.

[66], [73], [77], [78],

[91], [102], [106],

[110]

Robotic navigation These solutions employ robotic systems, such as robots, smart canes, scooter, and suitcases, which are

equipped with sensors to assist BVI in navigating their environment.

[59], [64], [67], [68],

[70], [78], [87], [89],

[99], [104], [106],

[107], [112]

Smartphone based Equipped with cameras and sensors, tablets and smartphones can be considered versatile navigation

tools. These solutions offer assistance through devices that are widely available and familiar.

[63], [74], [75], [80],

[98], [102], [104],

[107]–[111]

conducted.

For example, [56] involved nine BVI participants on a uni-

versity campus to evaluate a soniﬁcation system and collect

feedback on pleasantness, annoyance, precision, quickness,

and overall appreciation. Similarly, [57] conducted evalua-

tions with two BVI and three blindfolded participants in a

laboratory setting, focusing on the task success rates, com-

pletion times, and feedback from verbal and haptic cues.

The study by [58] included ﬁve BVI and three blindfolded

participants, achieving user satisfaction scores between six

and nine out of ten. Another study by [59] evaluated their sys-

tem with ten blindfolded participants, noting improvements in

acceptance and trust levels.

[62] found moderate to high satisfaction among eight

blindfolded participants, who found the system acceptable

and useful for indoor navigation. In contrast, [77] highlighted

that while users found the wayﬁnding function useful, they

expressed discomfort owing to the weight of the device.

Overall, the user-based evaluations indicated that participants

generally found the proposed systems beneﬁcial for naviga-

tion, with varying levels of satisfaction based on the speciﬁc

features and implementation of each system.

Some studies only conducted technical tests, without in-

volving direct user feedback. These studies are summarized

in Table 32. For example, [65] and [66] focused on the

technical performance of their systems and conducted tests in

controlled environments but did not report user satisfaction.

The absence of user-based evaluations limits our under-

standing of how these systems perform in real-world scenar-

ios and their acceptance among users. Future research should

aim to incorporate comprehensive user studies to complement

technical assessments and provide a more holistic view of a

system’s effectiveness and usability.

4) System prototype information

To provide a comprehensive understanding of the assistive

solutions proposed in the reviewed studies, we present the

information regarding the system prototypes in Tables 33-

37. These tables include data on the functionalities, sen-

sors used, computing resources, human-computer interaction

(HCI) mechanisms, assistive tools, battery life, and whether

the solutions are machine learning-based. Notably, the spec-

iﬁcations in these tables cover the entire assistive system,

and not just the localization and mapping components, as

presented in Tables 17-19.

Functionalities include the capabilities and features of the

assistive system, such as navigation, object recognition, and

obstacle avoidance. Sensors specify the types of sensors used

in assistive devices such as cameras, LiDAR, and IMUs.

Computing Resource indicates the hardware used for process-

ing, including local devices, such as smartphones and laptops,

as well as remote servers. HCI describes the interaction mech-

anisms used to provide feedback to the user, such as audio

and haptic feedback. The assistive tool details the form factor

of assistive devices, such as smart glasses, canes, and robot

systems. Battery Life provides information on the operational

duration of a device on a single charge. ML-based indicates

whether the assistive solution incorporates machine learning

algorithms.

a: Functionalities

The analysis of the data in the tables shows the diverse

range of approaches and technologies used to create assistive

systems for visually impaired navigation. Most systems focus

on navigation and obstacle avoidance, but many also include

advanced features such as scene understanding and social net-

working. The use of sensors is diverse, with RGB-D cameras

being the most commonly used because of their capability to

VOLUME 11, 2023 29

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 27. Effectiveness of localization and mapping techniques in indoor environments as indicated by the literature. (Part I)

Ref. Localization & mapping accuracy level Robustness level Considers

dynamic object

Running time

[57] High: Ensuring safe indoor navigation High: Utilizes robust ORB-SLAM2 technique

for reliable feature matching and adaptive nav-

igation.

•Affected by 0.15-1 sec transmission time to re-

mote server

[58] Not provide speciﬁc details High: Instant localization, robust map building •Real-time

[59] High: Conﬁrmed through user-guided tests Not provide speciﬁc details ✓Not mentioned

[60] High: With an average error of less than 1 meter High: Combined advantages of OpenVSLAM

and Colmap

•High: Average response time of 2 to 3 seconds

for localization

[61] High: With 0.62m for 3D and 1.24m for 2D High: Cartographer for mapping with optimiza-

tion techniques

•Moderate: For localization is under 0.25 seconds

[62] High: Accurate mapping, guiding users precisely

to target goals

High: Reliable performance with accurate map-

ping

•Moderate: Some delays in mapping, but overall

meets real-time requirements

[63] Moderate: Needs improved SLAM stability and

accuracy due to scaling issues

High: due to SLAM loop closing, real-time cor-

rection

•Not mentioned

[64] Moderate: Effective mapping and localization

but has deviations and mismatches impacting

overall accuracy

High: The integration of ORB-SLAM with

YOLO ensures robust navigation and obstacle

detection in various environments

•Moderate: Real-time map-building but slow path

planning

[66] High: With centimeter-level accuracy High: Real-time performance, centimeter-level

accuracy, semantic mapping integration, and re-

source optimization

•Real-time performance ensured through careful

computing power allocation

[69] Not explicitly mentioned High: Hector SLAM algorithm ensures robust

mapping and localization accuracy.

✓Real-time

[70] High: With signiﬁcant error reduction and effec-

tive 2D mapping alignment.

High: Enhanced by ﬂoor plan integration, error

reduction techniques, and superior performance

in real-time pose estimation.

•Real-time

[72] High: Achieved 96.3% localization accuracy;

outperforms other models despite pre-training

limitations.

High: Demonstrates robustness through accurate

SLAM technique, semantic mapping, and deep

learning integration for indoor localization.

•High: Longer computational time due to in-

creased complexity or resource requirements.

[73] High: Demonstrates superior accuracy compared

to ORB-SLAM2 in dynamic environments.

High: Demonstrates robustness through ad-

vanced SLAM techniques for obstacle removal

and dynamic environment adaptability.

✓Not mentioned

[75] High: Rigorous comparison and real-time re-

liance on CAD maps ensure precise localization.

High: Advanced localization and ﬂexible path

planning for accurate and safe navigation.

•Not mentioned

[76] High: Key-frame matching and ﬁsheye camera

enhance feature detection and accuracy.

High: ORB-SLAM2 for precise localization and

obstacle avoidance in effective indoor naviga-

tion.

Dynamic

obstacle in

experiment path

Short: Visual SLAM and dynamic subgoal selec-

tion optimize efﬁciency for real-time localization

and mapping.

[77] High: Superior to plane-based graph SLAM High: Efﬁciently addresses all 6-DOF and out-

performs traditional SLAM methods

•59.4 ms average per frame

[78] High: Demonstrated by superior performance in

pose estimation accuracy and robustness in vari-

ous environments.

High: Enhanced by integrating plane features

and employing a plane consistency check for

accurate pose estimation.

•Not mentioned

[79] Not mentioned Not mentioned ✓Real-time

[80] High: Utilizes visual landmarks, real-time data

analysis, addresses limiting factors

High: Robust to superﬁcial changes, requiring

updates only for major structural alterations.

•Moderate: Impacted by the number and type of

visual landmarks.

[83] High: Accurate localization and mapping

demonstrated through real-time indoor

experiments in static scenarios.

High: Hector SLAM ensures robustness in com-

plex indoor environments for accurate navigation

in static scenarios.

•Real-time

[84] High: <1m position error, <5°orientation error High: Integration of semantic SLAM, optimiza-

tion of semantic Point Clouds during loop clo-

sures

•Real-time (typically under 10 seconds, faster

near distinctive features)

[87] High: Cartographer SLAM and 360 LiDAR for

precise real-time mapping.

High: Cartographer ensures dynamic, real-time

LiDAR mapping with efﬁcient updates in unfa-

miliar environments.

•Real-time

[88] High: SLAM algorithm and low-drift IMU en-

able precise user pose estimation and environ-

ment mapping.

High: With real-time updates and effective

threshold management.

•Short: Navigation completed within 45 seconds

on average, well under the 2.5-minute cut-off.

[89] High: Highly accurate in real-time testing High: Integrating ORB-SLAM and YOLO en-

sures stability and accuracy.

•Not mentioned

[90] High accuracy feature-based visual SLAM esti-

mation

Moderate: Acknowledging limitations while uti-

lizing effective techniques, indicating room for

improvement.

✓Moderate: Accounting for variable GPU impact

and dependency on tracked objects.

capture both color and depth information, especially for the

localization and mapping components of assistive systems.

b: HCI

The HCI mechanisms vary, with audio feedback being the

most commonly used method. Several systems also use haptic

feedback and a few incorporate visual hints for users with

partial vision. These feedback mechanisms are essential for

real-time navigation assistance and for enhancing user experi-

ence. Several studies used multimodal feedback for real-time

navigation assistance, as indicated by the HCI column. These

include combinations of audio, haptic, and grounded kines-

thetics to enhance user experience and provide comprehen-

sive navigation aids. This multimodal approach ensures that

users receive complementary information, thereby enhancing

the robustness and reliability of assistive systems.

c: Assistive tool

The form factors of assistive tools vary among the stud-

ies. Wearable devices such as smart glasses and helmets

are designed to be worn on the body and provide hands-

free assistance. Handheld devices, such as smart canes, are

30 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 28. Effectiveness of localization and mapping techniques in indoor environments as indicated by the literature. NM indicates that the

information is not explicitly mentioned in the paper. (Part II)

Ref. Localization & mapping accuracy level Robustness level Considers

dynamic object

Running time

[91] High accuracy (RMSE 0.269), surpassing VINS-

Fusion and VINS-RGBD.

High robustness with RGBD-VIO, enhancing ac-

curacy and efﬁciency for assistivenavigation and

object manipulation.

•Not mentioned

[92] Achieving a 91% success rate in navigation tasks Demonstrating robustness, utilizing ORB-

SLAM2 algorithm enables real-time path

planning.

•Real-time

[93] Not speciﬁed Not speciﬁed •Not mentioned

[95] High: LiDAR and IMU data ensure precise lo-

calization and mapping.

High: Integrating multiple sensors ensures robust

localization for accurate navigation and collision

avoidance.

✓Not mentioned

[96] Lacking explicit discussion on accuracy, robustness, consideration of dynamic objects, and ML-based solutions.

[97] While numerical metrics are not mentioned, in-

dicators suggest potential accuracy.

High: OpenVSLAM framework ensures robust

real-time mapping and localization capabilities.

•Real-time

[98] High: Consistently achieves sub-1 meter local-

ization accuracy upon algorithm convergence.

High: Utilizing stable landmarks and a particle

ﬁlter ensures robust indoor localization.

•Real-time operation with accurate localization

post-algorithm convergence, demonstrating efﬁ-

cient performance on smartphones.

[99] High: Achieving accurate pose estimation with a

position error of 0.2 meters.

High: Enhancing robustness with integrated VIO

and Human Intent Detection for accurate pose

estimation and mode selection.

•Short: Real-time pose estimation with updates

every 22 milliseconds.

[100] Moderate Based on validation conducted in real-

world scenarios

Low: Performance degradation observed in spe-

ciﬁc scenarios due to narrow corridor and orien-

tation estimation issues.

✓Real-time

[102] High: 94-98% with relative errors of 1.6-2.6% High: Efﬁcient integration of visual SLAM, ob-

ject detection, and depth measurements for pre-

cise, reliable indoor navigation.

•Real-time

[103] Not mentioned Not mentioned •Not mentioned

[106] High: Superior pose estimation accuracy (mean

End Point Error Norm (EPEN): 2.63%) com-

pared to a state-of-the-art VIO (mean EPEN:

6.06%).

High: More stable performance (standard devi-

ation EPEN: 1.3%) than a state-of-the-art VIO

(standard deviation: 8.22%).

•Not mentioned

[107] Not evaluated Not evaluated •Real-time

[109] High: Precise self-positioning and mapping with

high accuracy in large-scale environments.

High: Superior robustness compared to conven-

tional monocular SLAM algorithms, ensuring

quick and reliable calculations.

✓Real-time

[110] High: Assessed via quantitative metrics and real-

world tests, achieving low navigation error and

high success rate.

High: Utilizing robust visual-inertial SLAM with

iOS ARKit for accurate real-time navigation and

obstacle avoidance.

NM Real-time per-frame pose estimation

[112] Not directly mentioned Moderate: Issues with mapping reﬂective or

transparent surfaces like glass windows.

•Not mentioned

traditional mobility aids enhanced by modern technology.

These smart canes include sensors to detect obstacles and

provide real-time feedback through vibrotactiles or steering.

This approach leverages the familiarity and comfort of using a

cane, while adding signiﬁcant technological advancements to

aid navigation and spatial awareness. Some prototypes incor-

porate both wearable and handheld components; for example,

in the study by [107], a Google Tango device was mounted

on the user’s chest while the user held a smart cane. These

prototypes are categorized as handheld devices because the

users’ hands are occupied. Robotic systems represent another

innovative factor. These can range from small mobile robots

in the shape of a suitcase that guides users through complex

environments to more substantial ride-on systems such as

autonomous wheelchairs or scooters.

d: Battery life

The battery life is a critical factor in assistive navigation

systems. These systems must be reliable to ensure contin-

uous assistance without frequent recharging interruptions.

The battery lives of the proposed solutions varied across the

reviewed studies. Some systems, such as those described by

[57], have reported a long battery life, which ensures that

the devices remain functional during extended use. However,

not all studies provide detailed information on battery life.

This lack of information can be a concern, as it leaves un-

certainty regarding the reliability of the device in real-world

scenarios. Additionally, some devices, such as those incor-

porating high-performance processors or multiple sensors,

may face challenges in maintaining a long battery life owing

to their higher power consumption. Systems such as those

described by [82], which use advanced components such as

the Nvidia Jetson AGX Xavier, may offer robust functionality,

but require careful management of power resources to ensure

adequate battery life.

e: Machine-learning approaches

Many systems leverage machine learning for functionalities,

such as object detection, scene understanding, and localiza-

tion. Algorithms such as YOLO, Faster R-CNN, and vari-

ous deep neural networks are commonly employed. These

machine learning-based solutions enhance the accuracy and

efﬁciency of the assistive systems. Table 38 categorizes the

machine learning approaches used in assistive devices, along

with their references. This categorization illustrates the diver-

sity of machine-learning techniques applied to improve the

functionalities of assistive systems for BVI navigation.

VOLUME 11, 2023 31

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 29. Effectiveness of localization and mapping techniques in outdoor and mixed environments as indicated by the literature. NM indicates that

the information is not explicitly mentioned in the paper.

Ref. Localization & mapping accuracy level Robustness level Considers

dynamic object

Running time

Outdoor

[71] High: Superior accuracy demonstrated across

multiple indicators and challenging conditions.

High: Demonstrates superior robustness, espe-

cially in the presence of dynamic objects and

changing illumination.

✓Moderate: Meets real-time requirements on pow-

erful devices, but slower on less capable hard-

ware.

[82] High: Global pose estimation within 80 cm ra-

dius of ground truth; veriﬁed in real-world sce-

narios.

Low: Navigation relies on SLAM-relative poses,

prone to instability with changing or occluded

features.

•Real-time

[101] SLAM used for localizing footage, creating spatial graphs, and generating realistic Deep Reinforcement Learning simulator data for pedestrian navigation training.

[105] Approximately 12cm error in localization accu-

racy

High: Robust in weakly textured environments •Not mentioned

Both (Indoor and Outdoor)

[56] Moderate: Support in measuring errors. Moderate: System evaluates errors reliably. •Not mentioned

[65] High: Precise ground truth trajectories, with min-

imal absolute error

High: Reliable trajectory extraction with low ab-

solute error

✓Not mentioned

[67] High: RMSE between 0.08 and 0.44 m, indicat-

ing high precision in indoor environments

High: SLAM-based system navigated complex

environments with precision and consistent suc-

cess across multiple trials

•SLAM operated at 1.4 Hz, which is sufﬁcient for

real-time use

[68] Low: SLAM struggles with dynamic environ-

ments

Low: SLAM demonstrates vulnerability in dy-

namic environments, with unstable navigation

and orientation errors compared to UWB posi-

tioning

✓High: Averaging 317 seconds for navigation

tasks in a dynamic environment

[74] High: Indoor localization with pre-built maps. High: VSLAM for indoor navigation enhances

robustness in GPS-degraded environments.

✓A real-time performance (approximate 20 fps) on

a smartphone.

[85] High: With an error of less than 0.5 meter High: Integration object detection and visual

SLAM for accurate navigation support.

•Real-time (with initialization under 2 seconds

and trajectory estimation under 1 second.)

[104] High: Accurate obstacle detection, extended

range, minimal error in indoor mapping, inte-

grated sensor data for dynamic environments.

High: Dynamic adaptation, effective in complex

and crowded environments with moving obsta-

cles.

✓Not mentioned

[108] High: Precise image feature extraction and mo-

tion trajectory reconstruction

High: Dependable for tracking position and ori-

entation.

NM Real-time

[111] Not mentioned Not mentioned ✓Not mentioned

IV. FUTURE OPPORTUNITIES

An effective navigation system for BVI people needs to

meet mobility metrics, such as decreasing navigation time,

decreasing navigation distance, decreasing contact with the

environment, and increasing walking speed. These systems

must be highly accurate and efﬁcient in complex situations,

such as crowded places and changing light and weather condi-

tions. At the same time, assistive aids should be comfortable,

easy to use, unobtrusive, cost-effective, lightweight, and re-

duce cognitive load.

In this review, we examined publications that employed

SLAM techniques in their navigation approaches. One of the

distinct advantages of SLAM is its applicability in diverse

locations without the need for pre-built maps or additional

infrastructure such as Bluetooth beacons or RFID tags. How-

ever, there is still room for improvement in various aspects of

these systems, including their ability to handle complex sce-

narios, provide accurate obstacle information, and seamlessly

transition between indoor and outdoor environments. This

section discusses the open problems and research directions

identiﬁed during the SLR.

Challenge scenarios and real-world studies Navigating

crowded environments remains a signiﬁcant challenge for

the visually impaired and studies addressing this issue are

limited. Evaluations are often conducted in controlled settings

rather than real-world scenarios. Future research should focus

on developing and testing solutions in high-trafﬁc public

places such as train stations and shopping malls. Furthermore,

addressing challenging conditions, such as changes in illu-

mination, low-light scenarios, high-speed dynamic objects,

and complex backgrounds, can enhance the robustness and

versatility of navigation systems. Techniques such as image

enhancement for ORB points and LSD line feature recovery

used in agricultural environments [116] can be adapted for

visually impaired navigation.

Future research should prioritize real-world testing and

validation by conducting extensive ﬁeld tests in high-trafﬁc

public areas, such as train stations, airports, and shopping

centers, to evaluate the system performance under diverse

environmental factors. Handling dynamic and complex en-

vironments requires the development of algorithms that can

adapt to rapid changes such as sudden variations in lighting,

moving objects, and different background textures. Machine

learning models that can learn and predict common patterns

in dynamic environments can improve system responsiveness

and reliability.

To enhance robustness under low-light and variable light-

ing conditions, one solution is to integrate advanced image

enhancement techniques and utilize sensors complementary

to traditional cameras, ensuring better obstacle detection and

navigation assistance in poorly lit areas. It is essential that

these enhancements do not compromise the real-time perfor-

mance of the system and maintain seamless and immediate

feedback for the user.

Long-term navigation The development of solutions that

are effective over extended navigation periods is critical to

achieve autonomous navigation. These solutions must ensure

accurate mapping and localization even when the maps are

32 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 30. User satisfaction evaluation. This table includes studies that conducted user-based evaluations with actual participants to assess the

effectiveness and satisfaction of the proposed systems - Part I.

Ref. # Participants Method of evaluation Experimental site User satisfaction

BVI Blindfolded User

study

Interview Survey

[56] 9 0 ✓•✓A university campus Participants’ feedback varied on pleasantness,

annoyance, precision, quickness, and overall

appreciation of soniﬁcation.

[57] 2 3 ✓• • Wearable Robotics and Autonomous Un-

manned Systems Laboratory at the University

of Science and Technology of China

No overall satisfaction score; details on task

success rates, completion times, and ver-

bal/haptic feedback.

[58] 5 3 ✓• • Not mentioned Scores 6-9 out of 10

[59] 0 10 ✓•✓Not mentioned Improved acceptance and trust levels noted

[60] 2 4 ✓• • New York University Langone Ambulatory

Care Center (A Complex hospital environm-

net)

Not mentioned

[62] 0 8 ✓ ✓ •A room Moderate to high satisfaction: Users found

the system acceptable and useful for indoor

navigation

[67] 12 12 ✓•✓Hallways constructedwith cardboard, outdoor Novice and expert users noted usability chal-

lenges due to weight, but conﬁdence and

workload improved

[68] 8 0 ✓ ✓ ✓ A hallway in the Boai Campus BIO–ICT

Building on the campus of National Yang

Ming Chiao Tung University, Taiwan

Participants found the proposed route easy to

navigate, with low perceived difﬁculty and

medium conﬁdence. Most intend to use it

again.

[74] 20 0 ✓•✓Ofﬁce area and simulated outdoor scenario Positive feedback on usabilityand navigation;

desire for detailed tutorials; satisfaction with

daily use, challenges with multi-ﬂoor naviga-

tion.

[75] 4 0 ✓•✓In a corridor All subjects found haptic instructions intu-

itive, enhancing safety and reducing hesitation

compared to audio, though some suggested

design improvements.

[77] 0 7 ✓•✓Various indoor places Users ﬁnd the wayﬁnding function useful, but

discomfort due to weight is a signiﬁcant con-

cern.

[82] 3 0 ✓ ✓ •Two different crosswalks Intuitive, easy-to-understand verbal instruc-

tions, enhances street crossing safety

[87] 7 0 ✓ ✓ •In unfamiliar building High satisfaction with PathFinder’s navigation

assistance, intersection detection, and audio

feedback.

[88] 0 6 ✓•✓In a conﬁgurable 12ft ×17ft room High conﬁdence, ease of use, and perfor-

mance rated positively; verbal overview and

haptics well-received for navigation assis-

tance.

updated over a longer navigation duration. To address this

challenge, researchers can leverage the proposed solutions

in robotics, such as that presented in [117], which intro-

duced a novel long-term SLAM system with map prediction

and dynamic removal, thereby allowing wheelchair robots

to maintain precise navigation capabilities over extended

periods. Future research should focus on developing robust

algorithms for continuous map updates and maintenance,

including strategies for handling environmental changes over

time. This involves dynamic object removal and adaptive map

reﬁnement to ensure the accuracy and relevance of navigation

maps.

Deep learning integration The integration of deep learn-

ing with SLAM algorithms for BVI navigation requires fur-

ther investigation. Deep learning offers a versatile approach

for enhancing various aspects of SLAM such as precise pose

estimation under challenging conditions, relocalization, and

loop-closure detection. Despite challenges, such as the need

for large, accurately labeled datasets, the black-box nature

of deep-learning models, and the computational intensity of

SLAM systems, the association between deep learning and

SLAM holds promise for advancing navigation solutions for

the visually impaired, particularly in challenging scenarios.

Future research should focus on developing more efﬁcient

deep-learning models that can operate effectively with limited

computational resources and real-time constraints. In addi-

tion, creating large-scale, accurately labeled datasets tailored

for BVI navigation is crucial for training robust models.

Addressing the interpretability of deep learning models can

also enhance the trust and transparency in these systems.

Collaboration among machine-learning experts, roboticists,

and vision scientists can drive the development of innovative

algorithms that leverage deep learning to enhance the relia-

bility and accuracy of SLAM-based navigation aids for the

visually impaired.

Indoor and outdoor navigation integration Seamless

VOLUME 11, 2023 33

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 31. User satisfaction evaluation. This table includes studies that conducted user-based evaluations with actual participants to assess the

effectiveness and satisfaction of the proposed systems - Part II.

Ref. # Participants Method of evaluation Experimental site User satisfaction

BVI Blindfolded User

study

Interview Survey

[91] 0 5 ✓• • In a laboratory The signiﬁcant improvement in success rate

and task completion time, from 32% to 96%

and 29.1s to 15.6s respectively, demonstrates

the effectiveness of the proposed solution in

aiding wayﬁnding and object manipulation.

[95] 14 0 ✓•✓A short route in a controlled and long route in

a real-world public space.

Participants expressed high levels of content-

ment with the system’s usability, effective-

ness, and overall user experience in the study.

[97] 0 1 ✓• • Not mentioned Assessed through successful task completion,

yet occasional false positives slightly affect

conﬁdence.

[110] 10 1 By sighted With

BVI

•In the Rhodes Research Center at Clemson

University

An online interview with 10 BVI individ-

uals via Zoom guided the choice of a 3D

perception-enabled mobile platform with a

speech-auditory interface.

transitions between indoor and outdoor environments are

crucial for enhancing the independence and mobility of BVI

individuals. However, most studies in our SLR have focused

primarily on indoor environments. Future research should

aim to develop solutions that provide uniﬁed and consistent

navigation experience in both indoor and outdoor settings.

Researchers should explore the integration of robust sensor

fusion techniques and adaptive algorithms capable of han-

dling the different conditions and challenges of these environ-

ments. Additionally, extensive real-world testing in various

settings is essential to ensure the practicality and reliability

of these solutions.

Obstacle detection Achieving detailed knowledge of ob-

stacles and their characteristics is essential for BVI peo-

ple. Although some studies included in the SLR addressed

obstacle detection, the depth and accuracy of the obstacle

information provided may still be limited. To address the need

for more detailed and accurate obstacle information for BVI

individuals, future research should focus on advancing SLAM

algorithms to deliver context-aware obstacle detection. This

involves integrating semantic understanding with precise spa-

tial measurements, allowing the system to identify and inter-

pret the nature and signiﬁcance of obstacles accurately.

Researchers should explore methods to enhance the real-

time processing capabilities to ensure that the system can

operate effectively in dynamic and complex environments.

Additionally, it is crucial to develop algorithms that can learn

and adapt to various obstacle types and scenarios through

continuous data collection and machine learning.

Drawing inspiration from approaches used in robotics and

autonomous drones, such as the real-time metric-semantic

SLAM demonstrated by [118], can provide valuable insights.

This approach integrates semantic understanding with pre-

cise distance and spatial relationship measurements, enabling

more comprehensive and reliable navigation aids. Therefore,

future research should prioritize improving both the depth

and accuracy of obstacle information, while ensuring robust

real-time performance and adaptability to various real-world

conditions.

Semantic information integration Integrating semantic

information into SLAM algorithms can signiﬁcantly enhance

the performance and robustness of navigation systems for

BVI individuals. This information can be used to reﬁne the

mapping and localization processes and enhance the overall

reliability of navigation in complex environments. For in-

stance, semantic information can help reject outliers in loop

closure detection, which is a crucial step in SLAM, which in-

volves identifying and matching previously visited locations.

To advance this area, future research should focus on de-

veloping advanced techniques for semantic data extraction

and integration within the SLAM frameworks. Researchers

should also explore methods to ensure real-time performance

while maintaining the accuracy and detail of the semantic

information.

Product development and collaboration Notably, all the

reviewed approaches were prototypes in the early stages of

research and are not yet practical. This might be due to

the absence of a uniﬁed community or group dedicated to

solving the BVI navigation challenges. Much of the work

in this domain has been carried out by academic groups

or small companies that often fail to produce feasible ﬁnal

products. This underscores a signiﬁcant future opportunity to

develop collaboration and to bridge the gap between research

and practical implementation. Additionally, efforts should be

made to develop standardized evaluation metrics and proto-

cols to ensure that the developed systems meet real-world

needs and can be effectively transitioned from prototypes to

market-ready solutions. Encouraging partnerships with tech-

nology companies can also accelerate the commercialization

process. These partnerships provide the necessary support to

bring innovative solutions to the market.

In conclusion, the future of SLAM for the visually impaired

navigation is promising. Continued research efforts have the

potential to develop SLAM algorithms tailored for BVI navi-

gation, empowering visually impaired individuals with a safe

and independent means of navigating their surroundings.

34 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 32. User satisfaction evaluation. This table includes studies that primarily conducted technical tests without direct user-based evaluations.

Ref. # Participants Method of evaluation Experimental site User satisfaction

BVI Blindfolded User

study

Interview Survey

[61] The technical test was conducted in the corridor environment of a typical ofﬁce building. Real-world user satisfaction was not directly assessed.

[63] 78 0 • • ✓Surveyed BVI online using LymeSurvey

to understand social networking needs

Not mentioned; the focus is on system

development

[64] 0 1 ✓• • A laundry room, H-shaped hallway, class-

room, and T-shaped hallway

Not mentioned; the focus is on system

development

[65] Not a user-based evaluation; the focus is on a technical test.

[66] Not explicitly mentioned; the focus is on a technical test.

[69] Not explicitly mentioned Laboratory Not mentioned

[70] The focus is on a technical test The Engineering East Hall of Virginia

Commonwealth University

Technical focus only

[71] Focuses on technical performance; real-world user satisfaction not directly assessed.

[72] The technical test was conducted in accommodation and ofﬁce buildings. Real-world user satisfaction was not directly assessed.

[73] The technical test was conducted in a university lobby. Real-world user satisfaction was not directly assessed.

[76] Unspeciﬁed 0 ✓• • Not mentioned Not mentioned

[78] The technical test was conducted on seven datasets collected internally. Real-world user satisfaction was not directly assessed.

[79] The test was conducted in the College of Computer and Information Sciences at King Saud University, but user satisfaction was not directly assessed.

[80] 1 5 Data was collected by participants to simulate indoor navigation, followed by ofﬂine analysis.

[83] The technical test was conducted in various indoor environments. Real-world user satisfaction was not directly assessed.

[84] The technical test was conducted in a corridor environment. Real-world user satisfaction was not directly assessed.

[85] The technical test was conducted in an ofﬁce room and on the KITTI dataset. Real-world user satisfaction was not directly assessed.

[89] The technical test was conducted on the KITTI-02 dataset. Real-world user satisfaction was not directly assessed.

[90] The technical test included TUM RGB-D, Bonn RGB-D datasets, and real-life sequences, but didn’t directly assess user satisfaction.

[92] 3; Visual ability unspeciﬁed ✓• • Not explicitly mentioned User satisfaction not directly assessed;

technical tests show high success rates and

accuracy.

[93] 1; Visual ability unspeciﬁed ✓• • In a laboratory User satisfaction not assessed

[96] Initiating target user testing was not feasible as the system was in the experimental stage.

[98] 5 0 ✓• • Smith-Kettlewell building Not mentioned

[99] The technical test was conducted in the East Engineering Building at VCU. Real-world user satisfaction was not directly assessed.

[100] 0 1 ✓• • In a laboratory The technical test was conducted. Real-

world user satisfaction was not directly

assessed.

[101] Paper develops a Reinforcement Learning environment to create a navigation assistant tailored for the BVI, without user-based evaluation.

[102] Technical tests conducted on the Karlsruhe dataset, indoor recorded dataset, and in a house; user satisfaction not assessed.

[103] Technical tests conducted in a research building; user satisfaction not assessed.

[104] Not explicitly mentioned; the focus is on a technical test.

[105] 1; Visual ability unspeciﬁed Not explicitly mentioned; the focus is on a technical test.

[106] The technical test was conducted on seven datasets collected in two buildings. Real-world user satisfaction was not directly assessed.

[107] 0 Unspeciﬁed ✓• • In various indoor environments (university

campus, hotel, ofﬁce building)

Not assessed

[108] The technical test was conducted in an ofﬁce and pedestrian street. Real-world user satisfaction was not directly assessed.

[109] The technical test was conducted in a laboratory. Real-world user satisfaction was not directly assessed.

[111] Real-world user satisfaction was not directly assessed.

[112] Technical tests conducted in a corridor; user satisfaction not assessed.

V. CONCLUSION

This study presents a systematic literature review of recent

studies on SLAM-based solutions for BVI navigation. Ex-

cluding papers published before 2017, this review focused

on the latest advancements, innovations, and considerations,

resulting in a more relevant and comprehensive understanding

of the current state of research. The insights provided by this

systematic literature review are intended to guide researchers

in the academic and research communities. They inform the

existing gaps and future opportunities to address the chal-

lenges faced by SLAM-based assistive solutions.

Relevant data were extracted from 54 selected studies that

adhered to the SLR selection criteria to address the research

questions. By analyzing the selected papers based on their

SLAM techniques, we observed that the majority of the stud-

ies utilized visual SLAM techniques, such as ORB-SLAM3,

owing to their advantages for visual sensors.

Several studies have introduced novel strategies for ad-

dressing localization and mapping challenges tailored to the

speciﬁc requirements of their research, whereas certain stud-

ies have employed existing spatial tracking frameworks to

develop navigation solutions. We also investigated the advan-

tages and limitations of the SLAM techniques, as highlighted

in the studies under review. Notably, most studies have lever-

aged accurate localization features of SLAM.

We investigated the challenging scenarios encountered by

SLAM-based navigation systems, which have been addressed

in the literature. Additionally, we discussed practical con-

siderations that affect the usability and adoption of these

systems. Furthermore, we analyzed how the proposed SLAM-

based solutions improve the mobility and navigation of vi-

sually impaired individuals. We evaluated the effectiveness

of these solutions in real-world scenarios and assessed the

user satisfaction to understand their practical impact on BVI

VOLUME 11, 2023 35

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 33. System prototype information for wearable devices - Part I.

Ref. Functionalities Sensors Computing resource HCI Assistive tool Battery life ML-based

[57] Navigation: positioning and wayﬁnding.

Multi-target recognition: object localiza-

tion, face recognition, and scene text

recognition.

RGB-D camera, Ul-

trasonic sensor

Embedded computer, remote

server

Audio,haptic Electronic glasses

and leg-mounted

haptic modules

Over 12 hours un-

der typical usage

Multi-target recognition

[58] Real-time perception, remote assistance

(leveraging WebRTC protocol), live

broadcasts, chatrooms, real-time tagging

A depth, an RGB, and

four gray scale cam-

eras, an IMU

Microsoft Hololens2 device,

GPU

Audio Microsoft

Hololens2

[60] Visual-based localization, location estima-

tion, direction estimation, and navigation

support

Phone’s camera Cloud server and Nvidia Jet-

son AGX Xavier

Audio Android applica-

tion

NetVLAD for global descriptors and Su-

perPoint for local descriptors to aid in the

localization process

[62] Active navigation with sub-goal infer-

ence, context-aware Object Relation Prior

Knowledge

RealSense D435i

RGB-D camera

Nvidia Jetson AGX Xavier Audio Helmet; white

cane for obstacle

avoidance

An unbiased Scene Graph Generation

(SGG) model to create scene graphs, then

aggregates them into an Object Relation

Knowledge Graph

[63] Scene description, face recognition, opti-

cal character recognition, obstacle recog-

nition, social networking, remote assis-

tance

IMU, stereo, IR

(depth) camera

Raspberry Pi4, Cloud server Audio,tactile Smartphone Faster RCNN: object detection, LSTM

RNN: scene description, imitation-

learning deep neural networks: navigation,

Google Tensorﬂow im2txt: scene

captioning, Neurotechnology’s Verilook

12.2: face recognition

[66] Real-time navigation, real-time semantic

understanding, voice interaction, precise

localization, real-time map generation

RGB-D camera High-performance portable

processor, cloud server

Audio A forehead

mounted

camera and

an earphone to

obtain the output

information

ENet for pixel-level semantic segmenta-

tion

[69] Mapping, path planning, obstacle avoid-

ance, transparent object detection, and

path following.

RGB-D camdera

(Asus Xtion Pro

live), Ultrasonic

sensor

Raspberry Pi3 B+ Audio Head-mounted

camera

•

[71] Obstacle avoidance, scene perception, and

hierarchical localization services.

RealSense RGB-D-

IR camera, IMU, a

customized GNSS

receiver

A portable computer, Nvidia

Jetson TX2

Not provided Auxiliary glasses NetVLAD and Dense Desc for advanced

descriptor extraction.

[73] Object detection and people detection for

obstacle avoidance.

Two monocular cam-

eras

Not mentioned Audio Smart eyeglasses YOLOv2 for person detection.

[74] Obstacle avoidance, surrounding percep-

tion.

RGB-D camera,

IMU, GPS

A smartphone with Qual-

comm Snapdragon 820 CPU

2.0 GHz

Audio Eyeglasses-

mounted sensors

+ smartphone

PeleeNet + SDD for object recognition

[75] Adaptive artiﬁcial potential ﬁeld path

planning and semantic understanding.

Smartphone’s

camera, Gravity

sensor, Ambient light

sensor, Proximity

sensor, Gyroscope

Compass

A HUAWEI P20 smartphone

with Kirin 970 CPU

Audio,haptic ARCore-

supported

smartphone +

haptic gloves

Not mentioned

[76] Locating, way-ﬁnding, route following,

and obstacle detection.

Fisheye and depth

camera, ultrasonic

rangeﬁnder

An embedded CPU board Audio, visual hints Optical see-

through glasses

•

[82] Scene understanding, localization, object

detection, path planning, path following,

timely completion

Realseanse D435i

RGB-D camera,

compass sensor

(BNO055 Bosch)

Nvidia Jetson Xavier AGX,

NVidia

Audio Head-worn cam-

era

Bisenet & HarDNet for crosswalk, the end

of the crosswalk (a red texture plate), and

the crosswalk signal detection

36 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 34. System prototype information for wearable devices - Part II.

Ref. Functionalities Sensors Computing resource HCI Assistive tool Battery life ML-based

[85] Detecting and locating objects of interest; guiding

users efﬁciently to target objects.

Monocular camera Nvidia Jetson Xavier NX

Developer kit

Audio, Virtual Touch [122] Chest-mounted

camera

Pretrained YOLOv5 for object detection

[90] Localization and mapping in dynamic environ-

ments, obstacle avoidance, dynamic object track-

ing.

RGB-D camera Laptop Audio Smart glasses PanopticFCN for obtaining the prior dynamic ob-

ject information, OpenPose for obtaining a more

accurate speed estimation of dynamic moving peo-

ple

[91] Locating a target object, wayﬁnding, motion guid-

ance, and grasping the object.

Occipital-Structure

Core sensor with

a built-in Bosch

BMI055 IMU, a

color camera, and a

global shutter stereo

IR camera

Google Pixel 3 smartphone

equipped with a Snapdragon

845 processor and 4 GB

RAM.

Audio,haptic Hand-worn

device

TensorFlow Lite Object Detection API (MobileNet

SSD model) for detecting the target object

[92] Indoor navigation, real-time path planning, object

of interest detection

RealSense D435i

camera

Embeded Jetson nano 4GB,

remote server based on

Intel i7-8700 CPU, nvidia

GTX1080 GPU, 64 GB

DDR4 RAM

Audio,haptic Smart E-glasses Over 12 hours per

charge

MobilenetV3-Yolov4-Lite, based on YOLOv4 and

MobileNetV3 for object detection

[93] Detecting and describing objects in environments,

personalizing navigation through interactive dia-

logues and re-training, and locating users.

Google glasses cam-

era

GPU server Not mentioned Google glasses YOLOv4 and SSD for detecting and describe ob-

jects, a classical attention-based encoder-decoder

model with LSTM and ResNet [123] for image

captioning.

[97] Scene perception, obstacle avoidance, and localiza-

tion

RealSense R200

camera

Nvidia Jetson AGX Xavier

processor

Audio Smart glasses RFNet for generating semantic labels

[100] Semantic mapping and path planning, obstacle

avoidance, environment perception

RPLIDAR A2,

Microsoft Kinect VI,

ZED stereo camera

Laptop Audio TurtleBot2 robot

(to be replaced by

portable device) +

wearable sensors

YOLOv3 for landmark detection, Places365 for

place recognition

[105] Tracking blind pedestrians’ paths Hero3+ GoPro Not mentioned Not mentioned Wearable camera •

[108] Obstacle avoidance, OCR, path planning, and hu-

man assistance via web application.

Stereo camera Cloud server Audio Helmet-mounted

camera +

android-based

smartphone

Recurrent Convolutional Neural for object detec-

tion and recognition, [124], [125] for scene pars-

ing, [126], [127] for Optical Character Recognition,

[128] for currency recognition, and [129] for trafﬁc

light recognition

[109] Route guidance to a destination, obstacle avoidance Monocular camera A single CPU Not mentioned Wearable camera •

[111] Wayﬁnding and identifying short-term impedi-

ments with GeoNotify smartphone software.

Kinect camera Not mentioned Audio,haptic Wearable camera YOLOv4 Tiny for object detection

VOLUME 11, 2023 37

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 35. System prototype information for handheld devices - Part I.

Ref. Functionalities Sensors Computing resource HCI Assistive tool Battery life ML-based

[64] Vibrations and sounds for obstacle avoidance, de-

tailed mapping, real-time object recognition, and a

smart cane for spatial orientation

RealSense RGB-D

camera

Raspberry Pi Audio,tactile Smart cane YOLO: Object detection

[67] Obstacle avoidance, waypoint following,

indoor/outdoor navigation, key object detection,

user guidance through challenges

2D LiDAR, camera,

GPS antenna, IMU

A portable microcontroller vibrotactile,Audio,

Grounded kinesthetic

Augmented cane Microcontroller:

4.2 and motor:

5.2 hours

YOLOV3Tiny for object detection, a linear regres-

sion model for distance estimation

[70] Active steering for user guidance, obstacle avoid-

ance, and wayﬁnding.

Realsense D435

(RGB-D) Camera,

VN100 IMU

UP Board computer Audio,tactile Smart cane •

[72] Accurate object detection, semantic mapping, and

indoor localization services.

hand-held RGB-D

camera

Odroid XU3 board, remote

server (data processing)

Audio No device ConvNet for semantic information extraction and

location inference; Inception-v3 for object recog-

nition.

[77] Wayﬁnding 3D object detection SwissRanger

SR4000 3D camera

Client: HP Stream 7 tablet,

server: Lenovo ThinkPad

T430 laptop (Intel i5-3320M

2.6GHz CPU, NVS 5400m

with 96 CUDA core)

Audio Smart cane •

[78] Pose estimation, obstacle detection and avoidance,

wayﬁnding

SwissRanger

SR4000 camera,

IMU (VN-100

of VectorNav

Technolofgies)

Up Board computer Audio Computer-vision-

enhanced white

cane

•

[80] Wayﬁnding and localization. iPhone 11 Pro sen-

sors

iPhone 11 Pro Not mentioned iPhone 11 Pro YOLOv2 for object detection to facilitate effective

localization

[83] Safely navigate to destinations in static unfamiliar

areas and identify surrounding objects.

Neato XV-11

LiDAR, ultrasonic

sensor, Raspberry pi

camera (CameraPi)

Raspberry Pi3 B+ Audio Sensors attached

to a white cane

Long battery life

expected

Tiny YOLOv2 for predicting object class.

[87] Intersection detection and sign recognition 360°LiDAR, iPhone

12 Pro camera

Nvidia RTX 3080 graphic

board

Audio, Handle interface Suitcase-shaped

robot

Low: 2.6 hours EasyOCR and YOLOv5 for sign recognition

[88] Finding socially preferred chairs RGB-D from the

RealSense D455 and

IMU from the T265

cameras

Dell G15 laptop with an

RTX 3060 GPU

Audio,haptic Robotic cane - Detectron2 for object detection and Mask-RCNN

for obtaining masks for classiﬁcation.

[89] Multi-sensory guidance, obstacle avoidance, real-

time target detection

RBG-D camera Uzel US-M5422 edge server,

Raspberry Pi 4B

Audio,tactile Smart cane YOLO for target detection

[95] Collision risk prediction, directional guidance,

mode switching, obstacle avoidance, real-time

feedback.

Two RealSense D435

RGB-D cameras,

IMU, LiDAR

Laptop (Intel Core i7-8750H

CPU @ 2.20GHz, NVIDIA

GeForce GTX 1080 Mobile

GPU)

Audio,tactile A rolling

suitcase-shaped

device

YOLOv3 for detecting surrounding pedestrians.

[98] Real-time localization and turn-by-turn directions. iPhone 8’s IMU and

rear-facing camera

iPhone 8 Audio Smartphone Low: 13% usage

in 16 minutes.

Not explicitly mentioned

[99] Wayﬁnding, human intent detection, and human-

robot interaction

RealSense

D435 Camera,

IMU (VN100

of VectorNav

Technologies, LLC)

UP Board computer Audio, motorized rolling tip White-cane

mounted camera

•

38 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 36. System prototype information for handheld devices - Part II.

Ref. Functionalities Sensors Computing resource HCI Assistive tool Battery life ML-based

[102] Real-time localization, navigation, object detec-

tion, and distance-depth estimation, using a single

monocular camera.

Monocular camera Intel i7 processor Audio Smartphone ACF detector for object detection to identify trained

objects of interest for localization.

[106] Pose estimation, wayﬁnding assistance Time-of-

ﬂight camera

(SwissRanger

SR4000), IMU (VN

100 of VectorNav

Technologies, LLC)

UP Board computer Not mentioned Computer-vision-

enhanced white

cane

•

[107] Indoor mapping, path planning, control panel inter-

face, and object avoidance.

Wide-angle lens

camera, gyroscope,

accelerometers, and

infrared sensor on

Google Tango, IMU

on the cane.

Google Tango,

microcontroller

Audio,haptic Smart cane +

Google Tango

Lasts

approximately 17

hours

•

[110] Global path ﬁnding, local path re-planning, and

obstacle avoidance.

Camera, IMU, inbuilt

3D LiDAR from

iPhone 12 Pro Max

Smartphone, AWS Lambda

on cloud

Audio iPhone 12 Pro

Max

SFSpeechRecognizer from iOS for speech-to-text,

ResNet for extracting feature representation of

each viewpoint for scene-graph map construction,

EnvDrop for path exploration, and reinforcement

learning for training the Vision-Language Naviga-

tion agent to navigate indoor environments based

on language instructions.

VOLUME 11, 2023 39

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 37. Prototype information for Robot systems, ride-on systems, and setups without specific devices or where device information is not mentioned.

Ref. Functionalities Sensors Computing resource HCI Assistive tool Battery life ML-based

Robot systems

[59] Considerate navigation, spatial risk mapping, adap-

tive motion control

A 2D range sensor,

2 RGB-D RealSense

D435 cameras

Two notebook PCs Audio Wheeled guide

mobile robot

Pedestrian detection (OpenPose), OpenCV ObjDe-

tect Module Face Recognition, Yolact obstacle

recognition, Speech recognition

[61] Localization LiDAR, cameras Nvidia Titan X GPU No device No device GAN-based localization

[68] UWB beacons for audio-based environmental in-

formation, dynamic obstacle avoidance, wall fol-

lowing, adjustable speed, and emergency stop.

Velodyne LiDAR

VLP16, RealSense

D345 depth camera

an Intel NUC computer,

Nvidia Jetson TX2,

Raspberry Pi3

Audio,haptic Guiding robot Reinforcement learning

[79] Robot navigation, obstacle avoidance, path plan-

ning, and user interaction

Encoder, IMU, laser

distance sensor, cam-

era

Raspberry Pi 3 Model B and

B+

Audio Smart robot

(Turtlebot3

robot)

•

Ride-on systems

[104] Autonomous navigation, real-time mapping and lo-

calization, obstacle avoidance, accurate steering.

IMU, MPU-9250,

stereo camera, laser,

LiDAR

Nvidia Jetson TX2, Rasp-

berry Pi, and Arduino.

Steering control Intelligent

autonomous

scooter

•

[112] Navigation, path following, obstacle avoidance. Hokuyo’s URG-

04LX-UG01 Laser

Range Finder (LRF)

sensor, Microsoft

LifeCam HD-5000

USB camera, MTi-

30 Attitude Heading

Reference System

(AHRS) IMU sensor

from Xsens

PC Autonomous navigation Person

carrier robot

(wheelchair)

•

Unspeciﬁed setups

[56] Navigation Camera Smartphone Audio Smartphone ﬁll this cell

[65] No prototype implemented; the proposed approach for trajectory forecasting was tested with a robot. AlphaPose: to detect and track the nearby people

appearing in each frame; PSPNet: to segment scene

semantics; Monodepth2: to estimate depth from

the monocular RGB frames; Transformer-based

encoder-decoder neural network model: contain-

ing a novel cascaded cross-attention mechanism to

fuse encodings of different modalities for trajectory

forecasting.

[84] Real-time global localization ZED2 RGB-D, IMU Nvidia Jetson AGX Xavier

microprocessor

No device No device MobileNetV2 with PPM for constructing semantic

point cloud

[96] Route planning and obstacle avoidance. RGB-D camera,

IMU, LiDAR

Not mentioned Audio No device Not mentioned

[101] Paper develops a Reinforcement Learning environment to create a navigation assistant tailored for the BVI community, without prototype implementation.

[103] Enhanced indoor localization and navigation using

environmental texts.

Smartphone and ZED

cameras

Nvidia Jetson TX2 No device No device No device CNN for text detection and recognition

40 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

TABLE 38. Categorization of machine learning techniques used in

assistive solutions

Technique References

Object detection

YOLO [64], [89]

YOLOv2 [73], [80], [83]

YOLOv3 (Tiny) [67], [95], [100]

YOLOv4 (Tiny) [93], [111]

YOLOv5 [85], [87]

Yolact obstacle detection [59]

(Faster) RCNN [63], [108]

Bisenet & HarDNet: crosswalk and signal detection [82]

Detectron2 [88]

TensorFlow Lite API [91]

MobileNetV3-Yolov4-Lite [92]

ACF detector [102]

CNN for text detection [103]

RCNN [108]

Object recognition

Multi-target recognition [57]

Inception-v3 [72]

PeleeNet + SDD [74]

EasyOCR: sign recognition [87]

Face recognition

OpenCV ObjDetect Module Face Recognition [59]

Neurotechnology’s Verilook 12.2: face recognition [63]

Semantic segmentation and scene understanding

LSTM RNN: scene description [63]

PSPNet: segment scene semantics [65]

ENet: pixel-level semantic segmentation [66]

MobileNetV2: constructing semantic point cloud [84]

Mask RCNN [88]

PanopticFCN [90]

RFNet: generating semantic labels [97]

Scene parsing using [124], [125] [108]

ResNet: scene-graph map construction [110]

Image captioning

Google Tensorﬂow im2txt [63]

Classical model with LSTM and ResNet [123] [93]

Visual odometry and localization

NetVLAD: global descriptors [60], [71]

SuperPoint: local descriptors [60]

GAN-based localization [61]

Monodepth2: estimating depth from RGB frame [65]

Transformer-based model: trajectory forecasting [65]

Deep Descriptors [71]

ConvNet: location inference [72]

Places365: place recognition [100]

Reinforcement learning & other techniques

Reinforcement learning [68], [101], [110]

Speech recognition [59]

OpenPose: Pedestrian detection [59], [90]

Scene Graph Generation [62]

LSTM RNN [63]

Imitation-learning DNN [63]

AlphaPose: detecting and tracking pedestrians [65]

Linear regression model: distance estimation [67]

Optical Character Recognition [108]

Currency recognition [108]

SFSpeechRecognizer from iOS [110]

EnvDrop: path exploration [110]

mobility. Finally, we identiﬁed gaps, opportunities, and areas

of interest that could be explored further in future research,

such as addressing challenges in crowded environments, im-

proving real-world applicability, integrating deep learning,

and ensuring long-term navigation effectiveness in SLAM-

based solutions for visually impaired navigations.

Given the widespread application of SLAM in robotic,

autonomous drones, and auto-driving car navigation, these

techniques can be adapted to ensure safe and independent

BVI navigation. This is particularly important in dynamic

and challenging environments, including those with vary-

ing lighting conditions where research opportunities remain

abundant. The potential of integrating these techniques into

the navigation of visually impaired individuals continues to

be an open and promising avenue.

ACKNOWLEDGMENT

We would like to extend our sincere gratitude to Giovanni

Ciofﬁ, whose insightful feedback and thorough review sig-

niﬁcantly contributed to the reﬁnement of this manuscript.

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MARZIYEH BAMDAD is currently pursuing her

Ph.D. in computer science at the University of

Zurich, supervised by Prof. Davide Scaramuzza,

and serves as a research assistant at the Zurich Uni-

versity of Applied Sciences in Switzerland. Her

Ph.D. research is dedicated to developing inno-

vative solutions for visually impaired navigation,

harnessing the potential of Visual Simultaneous

Localization and Mapping (Visual SLAM) tech-

nologies.

DAVIDE SCARAMUZZA is a Professor of

Robotics and Perception at the University of

Zurich. He did his Ph.D. at ETH Zurich, a postdoc

at the University of Pennsylvania, and was a visit-

ing professor at Stanford University. His research

focuses on autonomous, agile microdrone naviga-

tion using standard and event-based cameras. He

pioneered autonomous, vision-based navigation of

drones, which inspired the navigation algorithm

of the NASA Mars helicopter and many drone

companies. He contributed signiﬁcantly to visual-inertial state estimation,

vision-based agile navigation of micro-drones, and low-latency, robust

perception with event cameras, which were transferred to many products,

from drones to automobiles, cameras, AR/VR headsets, and mobile devices.

In 2022, his team demonstrated that an AI-controlled, vision-based drone

could outperform the world champions of drone racing, a result that was

published in Nature. He is a consultant for the United Nations on disaster

response and disarmament. He has won many awards, including an IEEE

Technical Field Award, the IEEE Robotics and Automation Society Early

Career Award, a European Research Council Consolidator Grant, a Google

Research Award, two NASA TechBrief Awards, and many paper awards. In

2015, he co-founded Zurich-Eye, today Meta Zurich, which developed the

world-leading virtual-reality headset Meta Quest. In 2020, he co-founded

SUIND, which builds autonomous drones for precision agriculture. Many

aspects of his research have been featured in the media, such as The New

York Times, The Economist, and Forbes.

44 VOLUME 11, 2023

Bamdad et al.: SLAM for Visually Impaired People: a Survey

ALIREZA DARVISHY is professor for ICT Acces-

sibility and head of the ICT Accessibility Lab at

Zurich University of Applied Sciences in Switzer-

land. He serves an independent reviewer for Eu-

ropean research projects such as the Active As-

sisted Living (AAL) program, and is principle in-

vestigator of the “Accessible Scientiﬁc PDFs for

All” project, funded by the Swiss National Science

Foundation.

VOLUME 11, 2023 45

ResearchGate has not been able to resolve any citations for this publication.

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Full-text available

Jan 2023

Background Wearable obstacle avoidance electronic travel aids (ETAs) have been developed to assist the safe displacement of blind and visually impaired individuals (BVIs) in indoor/outdoor spaces. This systematic review aimed to understand the strengths and weaknesses of existing ETAs in terms of hardware functionality, cost, and user experience. These elements may influence the usability of the ETAs and are valuable in guiding the development of superior ETAs in the future. Methods Formally published studies designing and developing the wearable obstacle avoidance ETAs were searched for from six databases from their inception to February 2023. The PRISMA 2020 and APISSER guidelines were followed. Results Eighty-nine studies were included for analysis, 41 of which were judged to be of moderate to high quality. Most wearable obstacle avoidance ETAs mainly depend on camera- and ultrasonic-based techniques to achieve perception of the environment. Acoustic feedback was the most common human-computer feedback form used by the ETAs. According to user experience, the efficacy and safety of the device was usually their primary concern. Conclusions Although many conceptualised ETAs have been designed to facilitate BVIs’ independent navigation, most of these devices suffer from shortcomings. This is due to the nature and limitations of the various processors, environment detection techniques and human-computer feedback those ETAs are equipped with. Integrating multiple techniques and hardware into one ETA is a way to improve performance, but there is still a need to address the discomfort of wearing the device and the high-cost. Developing an applicable systematic review guideline along with a credible quality assessment tool for these types of studies is also required.

3DMovieMap: an Interactive Route Viewer for Multi-Level Buildings

Conference Paper

Full-text available