Integrating Building Information Model and Augmented Reality for Drone-Based Building Inspection

Abstract

Unmanned aerial vehicles (UAVs) have been widely accepted for building inspection in recent years. However, the advantages of UAV inspection are not fully leveraged because of the absence of related information to assist decision-making during the inspection process. To address the problem, this study proposes an augmented reality (AR) solution by integrating the UAV inspection workflow with the building information model (BIM) of the building of interest, which is driven to simultaneously navigate with the aerial video during inspection. The integration enables easy and straightforward retrieval of useful information from BIM to help better understand the risk issues detected from the aerial video. An overall algorithm pipeline is proposed to drive the connective animation of BIM and the aerial video. The pipeline includes a fast coordinate transformation algorithm (F-Trans) that simplifies the process of converting the WGS-84 coordinates collected by UAVs to BIM project coordinates. An AR system prototype is developed to demonstrate the specific ways of applying the above algorithm pipeline to support the UAV-based building inspection. The efficacy of the proposed solution has been validated by experiments. The results demonstrate that the F-Trans can achieve a sub-meter precision, and the average matching degree between the aerial video and BIM was 74.1%. The developed AR solution has a great potential to enable a more efficient, comprehensive, and unbiased UAV-based building inspection.

Full text

1
Integrating Building Information Model and Augmented Reality for Drone-Based Building 1
Inspection 2
Donghai LIU1, Xietian XIA2, Junjie CHEN3,*, and Shuai LI4 3
1 Professor, State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 135 Yaguan 4
Road, Tianjin 300350, China. Email: liudh@tju.edu.cn. 5
2 Ph.D. Student, State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 135 6
Yaguan Road, Tianjin 300350, China. Email: xiaxietian@tju.edu.cn. 7
3 Ph.D., State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 135 Yaguan 8
Road, Tianjin 300350, China. Email: chenjj@tju.edu.cn. 9
4 Assistant Professor, Department of Civil and Environmental Engineering, the University of Tennessee, Knoxville, 10
851 Neyland Dr., Knoxville, TN 37902, the United States. Email: sli48@utk.edu. 11
ABSTRACT 12
Unmanned aerial vehicles (UAVs) have been widely accepted for building inspection in recent years. However, 13
the advantages of UAV inspection are not fully leveraged because of the absence of related information to assist 14
decision-making during the inspection process. To address the problem, this study proposes an augmented reality 15
(AR) solution by integrating the UAV inspection workflow with the building information model (BIM) of the 16
building of interest, which is driven to simultaneously navigate with the aerial video during inspection. The 17
integration enables easy and straightforward retrieval of useful information from BIM to help better understand the 18
risk issues detected from the aerial video. An overall algorithm pipeline is proposed to drive the connective 19
animation of BIM and the aerial video. The pipeline includes a fast coordinate transformation algorithm (F-Trans) 20
that simplifies the process of converting the WGS-84 coordinates collected by UAVs to BIM project coordinates. 21
An AR system prototype is developed to demonstrate the specific ways of applying the above algorithm pipeline to 22
support the UAV-based building inspection. The efficacy of the proposed solution has been validated by 23

2
experiments. The results demonstrate that the F-Trans can achieve a sub-meter precision, and the average matching 24
degree between the aerial video and BIM was 74.1%. The developed AR solution has a great potential to enable a 25
more efficient, comprehensive, and unbiased UAV-based building inspection. 26
Keywords: Augmented reality (AR); Unmanned aerial vehicle (UAV); Building information modeling (BIM); 27
Build inspection; Decision support; Fast coordinate transformation. 28
INTRODUCTION 29
Visual inspection has long been used to detect structural defects to guide the rehabilitation of buildings. 30
Traditional visual inspection relies on field workers to manually check out the status and condition of the building 31
of interest. This practice is not only laborious and time-consuming but also prone to be dangerous in cases such as 32
exterior inspection of high-rise buildings. In recent years, unmanned aerial vehicles (UAVs), or commonly known 33
as drones, have been used to replace field workers for visual inspection (Duque et al. 2018a; O’Riordan-Adjah and 34
MacKenzie 2019). By reviewing the video recorded by an onboard camera installed on the UAV, inspectors are 35
allowed to fulfill their tasks in the office rather than onsite, which leads to an improvement of efficiency and a drop 36
of fatality related to falls for building inspection. 37
However, the practice of reviewing the collected video for structural defect detection can be error-prone if no 38
related information is provided to support decision-making. Without easy access to the information about the 39
building of interest, such as the material properties, object geometry and components location, it is difficult for the 40
inspectors to comprehensively evaluate the safety issues detected from the video. This might either lead to an 41
overestimation or an underestimation of the detected issues: While the former wastes unnecessary resources for 42
restoration, the latter can result in fatal hazards by allowing a serious defect to continuously deteriorate. As a result, 43
it is necessary to develop a means that can augment the aerial inspection video with easy-access building-relevant 44
information to enable an unbiased and comprehensive condition assessment. Not meeting the need will continue to 45

3
have the inspectors make decisions without enough information support, leading to biased judgments that could 46
jeopardize the safety of the building. 47
Recent developments of building information modeling (BIM) and augmented reality (AR) provide an 48
opportunity to address the above research need. BIM is an innovative approach to modeling building information 49
in a 3D object-based information system (Azhar 2011). As a 3D model that integrates information of a building 50
throughout its entire life cycle, BIM serves as a perfect source from which readily available information can be 51
retrieved to support decision-making. AR, as a technology that superimposes virtual objects onto the physical world 52
(Milgram et al. 1995), can enable the interaction between the virtual context and the real scene, and hence can 53
potentially be used to augment the aerial videos captured by UAVs with the virtual BIM model. In the authors’ 54
previous research (Liu et al. 2019), a novel AR framework was proposed to integrate BIM and UAV to assist the 55
inspection of water diversion projects. In the framework, the real-life scene captured by the UAV-borne camera is 56
augmented by a simultaneously navigated BIM. With such a virtual-real interaction, the AR framework provides a 57
visualized and integrative environment to support decision-making for safety inspection of water pipelines. 58
Enlightened by our previous research, this study intends to develop an AR solution for building inspection by 59
adopting the concept of connective animation of BIM and aerial video. Due to the differences in the structures of 60
interest regarding the scale, areas, height, etc., and the different inspection requirements, further development of the 61
original method is required from the following aspects. First, the original coordinate transformation workflow was 62
too complicated and tedious to be implemented; hence, it is important to investigate whether the original workflow 63
can be simplified at the scale of building. Second, since a building has a much smaller scale and presents a much 64
more complicated appearance than a water diversion project, it is unclear whether the matching between the 65
animated BIM and the aerial video in the building inspection scenario can still achieve performance comparable to 66
that observed in the previous study. Third, building inspection has different focuses from the water diversion 67

4
projects, and thus the last challenge rests upon how the original framework can be customized to develop an AR 68
system that can provide functionalities compliant with the specific demands of building inspection. 69
The address of the above three concerns forms our primary contributions. First, a fast coordinate transformation 70
algorithm (F-trans) has been proposed to simplify the procedure of converting WGS-84 coordinates collected by 71
UAVs to BIM project coordinates used by the 3D rendering engine. The simplified algorithm is easy and handy to 72
be deployed in different building projects. Second, the feasibility of the virtual-real interaction algorithm originally 73
proposed for water pipeline inspection has been experimentally testified in simultaneously matching a BIM model 74
of the inspected building with an aerial video (Liu et al. 2019), which extends the algorithm to the area of building 75
inspection. Third, an AR system prototype is developed to demonstrate the specific ways in which the integration 76
of UAV and BIM can support condition assessment and decision-making for building inspection. By combining 77
both the mobility of UAVs and the enormous easy-access information provided by BIM, the developed AR system 78
has great potential to improve the building inspection efficiency and promote the understanding of the detected 79
distress. 80
REVIEW OF RELATED TECHNOLOGIES IN BUILDING INSPECTION 81
Unmanned aerial vehicle (UAV) 82
UAVs are effective inspection platforms due to their merits of high mobility and wide coverage for video recording 83
or image capturing. They have been widely applied in the inspection of bridges (Khan et al. 2015; Duque et al. 84
2018b; Seo et al. 2019), buildings (Roca et al. 2013; Morgenthal and Hallermann 2014; O’Riordan-Adjah and 85
MacKenzie 2019), water diversion projects (Liu et al. 2019), dams (Henriques and Roque 2015), chimney 86
(Hallermann and Morgenthal 2013), photovoltaic panels (Aghaei et al. 2015), and high mast luminaires (HML) 87
(Otero 2015). Image processing techniques and artificial intelligence are being used to detect cracks and damages 88

5
from the visual assets collected by UAVs. For instance, Choi and Kim (2015) developed a UAV-based image 89
collection system for building exterior crack detection. Gopalakrishnan et al. (2018) integrated UAV inspection 90
with a pre-trained deep learning model to detect cracks from the collected aerial images. Morgenthal and 91
Hallermann (2014) outlined a principal approach to assessing the detection quality of UAVs based on automated 92
damage recognition using computer vision methods. Kang and Cha (2018) integrated ultrasonic beacons, a deep 93
convolutional neural network (CNN), and a geotagging method into a novel UAV inspection system, which can 94
perform in an environment where GPS signal is occluded for the effective detection and localization of structural 95
defects. Despite progress that has been made in recent years, existing studies fail to provide effective solutions to 96
augment the aerial video with abundant and intuitive information to support condition assessment and decision-97
making. In fact, while automated defect detection is important, it is also equally essential to provide relevant 98
information, such as geometric dimension and material composition, to empower the decision-making of engineers 99
and professionals when the videos are reviewed for further evaluation of the detected issues. As a 3D object-based 100
information system of the built environment, BIM serves as a potential source where readily available information 101
of the structure of interest can be retrieved. 102
Building information modeling (BIM) 103
BIM contains multi-source information of a building project throughout its life cycle, encompassing the phase of 104
planning, designing, construction, operation and maintenance, and even demolition (Azhar 2011). While it was 105
mainly used in the designing and construction phase in the past, BIM is gradually being adopted nowadays for the 106
operation and maintenance of building projects (Parsanezhad 2014). In (Chen et al. 2020), BIM was used for the 107
inspection and maintenance of fire safety equipment; visual inspection and a pictorial survey were combined with 108
BIM to assess the water ponding defect on a flat roof in (Ani et al. 2015); BIM was used as a knowledge repository 109
to store historical inspection records for the improvement of the inspection-repair process by (Zhan et al. 2019); 110

6
McGuire et al. (2016) used BIM to link and analyze data related to the inspection, evaluation, and management of 111
bridges for tracking and assessing the structural condition; Shim et al. (2017) developed a BIM-based bridge 112
maintenance system for cable-stayed bridges; Hsiao and Lin (2018) developed a web-based BIM models inspection 113
and modification management system. Existing studies demonstrate the potential of using the life-cycle information 114
from BIM to augment the UAV-based visual inspection. 115
Augmented reality (AR) 116
AR is a promising technique to improve the efficiency of visual inspection and to support decision-making. By 117
superimposing related information from the virtual context onto the real scene, inspectors are allowed to gain 118
comprehensive situational awareness about the environment. He et al. (2007) investigated the application of AR in 119
visualizing underground utilities in real-life contexts. Kopsida and Brilakis (2016) proposed a markerless mobile-120
based AR framework for building inspection in interior environments. Li et al. (2015; 2016) and Yuan et al. (2018) 121
used ground penetrating radars to map and characterize buried pipelines, which lays the foundation for visualizing 122
invisible underground utilities in AR. Li et al. (2018) developed a mobile-based AR system for underground pipeline 123
inspection and management. Kumar et al. (2019) proposed a sensor fusion based AR system for pipeline inspection 124
and retrofitting. Dang and Shim (2020) proposed a BIM-based innovative bridge maintenance system and used AR 125
devices to conduct automated inspection. 126
In literature (Milgram et al. 1995), AR is defined as a technique that "augments natural feedback to the operator 127
with simulated cues". In this sense, systems, methods, and technologies that facilitate humans’ understanding 128
towards the physical world by the interaction with virtual context can be considered as AR. Existing works mainly 129
focus on overlaying virtual information onto the real scene captured by handheld devices (e.g. smartphones) or 130
wearable equipment (e.g. AR Helmet); thus, it is uncertain whether they will be suitable to augment the aerial video 131
with virtual contents retrieved from BIM. In addition, it is necessary to develop an AR system customized to the 132

7
specific requirements of UAV-based building inspection. Liu et al. (2019) presented a dynamic BIM-augmented 133
UAV inspection method for large-scale infrastructure, which successfully addressed the information augmentation 134
issue by simultaneously navigating the virtual BIM model and the real-life inspection video. However, building has 135
a smaller scale than infrastructure (Liu et al. 2018), which leads to a closer location of UAV from the building and 136
a more complex geometry outline of the inspected target in the video frame. Hence it remains unclear whether the 137
method proposed for the case of infrastructure is suitable for building inspection. In addition, the coordinate 138
transformation procedure in the original method is too complicated to perform without the involvement of domain 139
experts. 140
PIPELINE OF UAV-BIM CONNECTIVE ANIMATION 141
Fig. 1 illustrates the overall pipeline of the proposed UAV-BIM connective animation algorithm. A UAV is 142
used to perform building inspection. During the inspection, an aerial video and the flight status of the UAV (position, 143
posture, field of view, etc.) are recorded. When the aerial inspection video is played and reviewed afterwards, a 144
BIM model of the building of interest is designated to simultaneously navigate with the aerial video by utilizing the 145
corresponding flight status data. The specific procedure is as follows. 146
(1) Coordinate transformation 147
Since the position coordinates collected by the UAV and the BIM model use different coordinate systems, the 148
WGS-84 coordinates collected by the UAV need to be converted to BIM project coordinates before they can be 149
used to guide the BIM model navigation. 150
(2) Camera parameters matching 151
The rendering of the BIM scenario is determined by parameters of a virtual camera in the 3D engine. The 152
specific forms of the parameters of the virtual camera are different from the flight status data collected by UAV. 153
For instance, the yaw, pitch, and roll angle are used to describe the posture of the UAV camera, while a camera up 154

8
vector and a line-of-sight vector are used for the virtual camera. Therefore, the collected data that describes the 155
UAV location, posture, and the field of view needs to be matched to the parameters used by the virtual camera. 156
(3) Connective animation 157
The flight status data after coordinate transformation and parameters matching is retrieved to drive the BIM 158
model to continuously and smoothly navigate along with the aerial video. 159
F-Trans: Converting WGS-84 to BIM project coordinates 160
Unlike a water diversion project that usually spans a long distance and a large area (Liu et al. 2018, 2019), the 161
floor space of building is usually quite limited. For example, New Century Global Center at Chengdu, one of the 162
single buildings with the largest floor area (Wikipedia 2020), “only” covers an area of 500×400 square meters, 163
which can be seen as a single spot compared with those water diversion projects that cover thousands of square 164
miles. Due to this characteristic, the selection of the reference ellipsoid will not have a significant influence on the 165
results of coordinates transformation in a building project. Hence, it is feasible to simplify the transformation 166
process by omitting the 3D transformation in the original method proposed by Chen et al. (2019). 167
A fast coordinate transformation algorithm (F-Trans) is proposed to convert the WGS-84 coordinates collected 168
by the UAV to BIM project coordinates. As shown in Fig. 2, F-Trans includes two steps, i.e., coordinate projection, 169
and plane transformation. In the first step, the geographic coordinates (typically denoted by longitude
lon
and 170
latitude
lat
) in the WGS-84 system are projected to a plane coordinate system, of which the coordinates are usually 171
denoted by x and y. With determined ellipsoidal parameters, projection principle, and central meridian, this 172
projection operation can be easily conducted by GIS (geographic information system) software. 173
The second step transforms the plane coordinates in the WGS-84 system to BIM project coordinates. This step 174
can be geometrically represented by a transformation between two different plane Cartesian coordinate systems, as 175

9
shown on the right-hand side of Fig. 2. The BIM project coordinate system (
O
as origin, and XBIM and YBIM as 176
two axes) is the result of translating and rotating the original coordinate system (O as origin, and X and Y as two 177
axes), respectively by a vector
(,)XY
and an angle

. As a result, the coordinates in a BIM project 178
coordinate system can be calculated by Eq. (1). 179
cos sin
( , ) ( , ) ( , )
sin cos


−

= +  


T T T
bim bim
x y x y X Y
180
Where,
(, )xy
and
(, )
bim bim
xy
are, respectively, the plane coordinates in the WGS-84 system and the BIM 181
project coordinates. The translation vector
(,)XY
and rotation angle

vary according to the specific 182
application scenario. Hence, these three unknown parameters need to be calculated based on at least two reference 183
points, of which the coordinates are known in both the BIM project system and the original system. In chapter 5, 184
the calculation process will be elaborated by a real-world case. 185
The height of a point in the BIM project system (
bim
h
) can be calculated by subtracting a value
H
from 186
the elevation value (
h
) collected by the UAV, as illustrated by Eq. (2). 187
= −
bim
h h H
188
Where the value
H
is calculated based on at least one reference point. 189
Virtual-real matching algorithms 190
As shown in Fig. 3, a virtual camera in a BIM rendering engine uses two aspects of parameters: physical 191
parameters and optical parameters. The physical parameters describe the location and the posture of the camera, 192
which include a position vector
peye
, a camera-up vector
vup
, and a line-of-sight vector
vat
. The optical 193
parameters define the projection system and the scope of rendering. A perspective projection system is used, and it 194
includes four parameters, i.e., fovV, aspect, near, and far. 195
(
1
)
(2)

10
Using the recorded data about the position and posture of the UAV, the physical parameters of the virtual 196
camera can be calculated by Eq. (3) ~ Eq. (5). 197
( , , )p=T
eye bim bim bim
x y h
198
sin( )sin cos( )sin cos
22
cos( )sin sin( )sin cos
22
cos cos
up

    

    


− − −


=
− − − −



v
199
(cos cos( - ),cos sin( - ),sin )
22
v

    
=T
at
200
Where,
( , , )
bim bim bim
x y h
is the position coordinates of UAV after transformation;

,

, and

describe the 201
posture of the UAV-borne camera, which are, respectively, the yaw, pitch, and roll angle. 202
The optical parameters of the virtual camera are calculated by Eq. (6). 203
/
min
   
   
   
=
   
   
+
   
RR
VR
aspect w h
fov fov
near
far
204
Where,
R
w
and
R
h
are respectively the width and the height of the imaging plane of the real camera;
R
fov
is 205
the field of view of the real camera; min is a minimal constant, and
+
represents infinity. 206
Connective animation 207
Fig. 4 illustrates an algorithm flowchart to drive the connective animation between the aerial video and the 208
BIM model. With the process started, a listening event is activated to detect whether the aerial video is being played. 209
If it is “Yes”, the time interval
t
is set as 2 s; on the contrary,
t
is set as 0. Next, the global sampling time 210
t
of the current frame of the video is calculated by adding the video start-recording time
0
t
to the current playing 211
progress
c
t
. The data of flight status, i.e., latitude, longitude, height, yaw, pitch, and roll, at time
tt+
is then 212
retrieved, which is denoted by
( , , , , , )
  
+=
tt
uav
d lat lon h
. Note that optical parameters such as fovV and aspect, 213
which are usually fixed values for a certain kind of UAV, have already been pre-programmed into the system. Next, 214
(3)
(4)
(5)
(6)

11
F-trans and the matching algorithm are executed to obtain the target camera status
+( , , )p v v
=
t t eye up at
S
. Then, 215
Tween.js is used to realize the smooth transition of the BIM camera from the current state to the target state
+tt
S
. 216
When the Tween object is instantiated, the transition duration should be set equal to
t
. At last, once the transition 217
process is finished, the program automatically executes the next cycle of the aforementioned process unless a ‘stop’ 218
event is activated. With this method, the connective animation between the aerial video and BIM model is realized. 219
AR SYSTEM PROTOTYPE FOR BUILDING INSPECTION 220
Based on the aforementioned algorithms, an AR system prototype has been developed to support UAV-based 221
building inspection. 222
System architecture 223
Fig. 5 shows the architecture of the system prototype. The AR system prototype adopts the B/S mode, which 224
comprises a database server and client-end web browsers. The database server stores inspection-related data to 225
support the AR inspection on the web browser. Through the web browser, inspectors can log in to the system to 226
view the aerial inspection video and implement the AR inspection. 227
System functionalities 228
The functionalities of the system prototype can be divided into four modules, i.e., data uploading module, AR 229
inspection module, query module, and system setting module. 230
(1) Data uploading. Via this module, users can upload the inspection-related data onto the database server, 231
which includes inspection specifications, the BIM model of the building to be inspected, UAV inspection videos, 232
and flight status data. 233
(2) AR inspection. Via this module, users can view the aerial inspection video with the corresponding BIM 234
model being simultaneously navigating and check properties of the inspected building from BIM, e.g. object 235

12
geometry, material properties and component location. Users can also assess structural conditions, make 236
comprehensive decisions, and generate inspection reports with the support of the retrieved information from BIM. 237
(3) Query. Users can check the historical inspection reports in this module. 238
(4) System setting. With this module, users can specify basic system parameters such as the field of view of 239
the UAV camera and coordinate transformation parameters. 240
System development 241
Fig. 6 illustrates the technical details for the development of the system prototype. The database server is 242
programmed with PHP to realize the functionality of data storage and retrieval. The HTML page on the client-end 243
web browser is developed with HTML, JavaScript, Ajax, and JSON. The data communication between the database 244
server and web browser (e.g. inspection specifications and reports, flight records, and algorithm parameters) is 245
realized by Ajax. The BIM model of the building of interest has been created in advance. By calling the Autodesk 246
Forge cloud-end Model Derivative API, the original BIM model is compressed and converted into a light-weight 247
format in order to be displayed on web browsers. The Autodesk Forge Viewer API, which is an encapsulation of 248
Three.js, is called to render and display the lightweight BIM model on the web browser. The Aliplayer API is used 249
to upload and play aerial inspection videos through the web browser. The Tween.js is utilized to drive the connective 250
animation between the aerial video and BIM. In this procedure, the flight status data is retrieved from the database 251
and processed by the Ftma.js to conduct the aforementioned F-trans and virtual-real matching. 252
EXPERIMENT VALIDATION 253
Experiments have been performed to validate the proposed solution for building inspection. The building of 254
interest is the student union at the University of Tennessee, Knoxville, which has a floor space of 110×131 m2, as 255
shown in Fig. 7. DJI Phantom 4 Pro was used for inspection, of which the onboard camera has a field of view of 256
84°, an equivalent focal length of 24 mm, and a video resolution of 1280×720. In this chapter, the precision of F-257

13
trans and the virtual-real matching accuracy are first evaluated; then the application of the developed AR system is 258
introduced; the application scope of F-trans and factors that affect the virtual-real matching are discussed in the last 259
part. 260
Coordinate transformation 261
To evaluate the precision of F-Trans, eight reference points were selected on the perimeter of the student union 262
(as shown in Fig. 8). The WGS-84 coordinates of these reference points were obtained via Google Map, while their 263
BIM project coordinates were retrieved from Autodesk Forge Viewer. Point #1 and #2 form a calculation set (as 264
listed in table 1), which is used to calculate the transformation parameters in Eq. (1), and the other points form a 265
test set. 266
By inputting coordinates of point #1 and #2 into Eq. (1), an equation set was obtained, as shown by Eq. (7). Its 267
solution can be easily calculated as
= 117.77


−
,
= 3164643.093−X
, and
=2544865.713Y
. 268
-72.37=777193.47cos sin
777193.47sin cos
-70
3985020.21
30.07 3985020.21
3985013..78=777176.80cos sin
777176.80sin cos
24
48 3985013.24




− + 

+ + 

− + 
=
=

+ + 

X
Y
X
Y
269
Using the obtained transformation parameters, the transformation results of the test set are listed in table 2. The 270
average error for X component and Y component is, respectively, -0.20m and 0.40m, which meets the requirement 271
for subsequent UAV-BIM connective animation. 272
Evaluation of virtual-real matching accuracy 273
The drone, DJI Phantom 4 pro, was operated to fly along the perimeter of the building of interest to record an 274
aerial video. During the process, the drone was programmed to automatically record its flight status in a .txt file. 275
The aerial video and the flight status data can be accessed with a micro SD card and uploaded onto the AR system 276
(7)

14
after the flight, with which the connective animation of BIM and the aerial video was realized by the proposed 277
method. A video demo can be found on https://youtu.be/ogULi6IyLvw. 278
Fig. 9 shows 24 pairs of screenshots of the simultaneously navigating BIM and aerial video with 5 seconds 279
interval. In each image pair, the playing progress of the video (the time) was marked on the top left, the first row is 280
the BIM model, and the second row is the image frame of the video. To quantitatively evaluate the matching 281
accuracy between the aerial video and the BIM model, an index called Intersection over Union (IoU) was adopted 282
to measure the level of alignment between the real building in the video frames and the virtual building in the BIM 283
model (Chen et al. 2019). The extracted structural of interest (SOI) from the aerial video frames is denoted by Suav, 284
while the SOI from BIM images is denoted by Sbim. The loU is defined as a ratio of the area of Suav

Sbim to the 285
area of Suav

Sbim (as shown in Eq.(8)). 286
()
()
uav bim
uav bim
A S S
IoU A S S

=
287
where A(x) is the area of region x, which can be reflected by the quantity of pixels in the region. 288
Fig. 10 visualizes the level of alignment by overlying the extracted building of interest from the BIM images 289
(the transparent red area) onto that extracted from the aerial images (the ground truth), where the IoU values are 290
annotated on the bottom-right corner. According to (Ferguson et al. 2019), an IoU value larger than 50% is regarded 291
as a satisfying result. The average value of IoU is 74.10% in this study, indicating that the BIM model and the 292
recorded aerial video have been matched well. 293
Application of the AR system prototype 294
Using the developed system prototype, users should upload and specify the BIM model, inspection 295
specifications, and relevant algorithm parameters in advance, since they are basic data that usually remains 296
unchanged for a certain building project and a certain type of UAV. Each time the UAV finishes onsite data 297
(8)

15
collection, inspectors should upload the flight status files and aerial videos into the system before implementing the 298
AR building inspection. 299
Fig. 11-13 present three screenshots of the developed AR system prototype. As shown in Fig. 11, the inspectors 300
can refer to the inspection specifications during the review of the aerial video, which is augmented by a 301
simultaneously navigating BIM model. Once a safety issue (e.g. crack, spalling, water accumulation or windows 302
crack) is detected from the video, the inspector can intervene to pause the navigation, and retrieve useful information 303
from the BIM model, e.g. check out properties of certain elements, or conduct distance measurement. Based on the 304
retrieved information, mechanical calculation of the structure can be done to assess the safety condition of the 305
building. 306
In addition, when a potential safety issue is detected, the inspectors can also use the “issues screenshots” 307
function (Fig. 12) to capture the screenshot of the displayed video and BIM model to record the detected issue. 308
Alongside the captured screenshot, the inspectors are asked to fill in and submit the related information such as 309
issue category, detail, and location. The detected issues will be gathered and displayed in the inspection reports area 310
(Fig. 13). In this page, the inspector can make condition assessment and give solution instructions for the detected 311
safety issues, based on the visual information from the aerial video and relevant information retrieved from BIM. 312
The inspection report of the current inspection can be submitted to the system. Historical inspection reports can be 313
queried from the query module to support a more comprehensive evaluation of the current condition of the building. 314
The above case study indicates that the developed AR system prototype can effectively improve the inspection 315
efficiency, help inspectors better understand the detected safety issues, and has the potential to lead to a more 316
comprehensive and unbiased condition assessment based on UAV-enabled visual inspection. 317

16
Discussion 318
Application scope of F-Trans 319
As mentioned before, the simplification adopted by F-Trans is feasible because of the relatively small scale of 320
building projects. However, the specific range within which the F-Trans is applicable remains unclear. Hence, the 321
application scope of the F-Trans is discussed in this section. A long-distance water diversion project (Fig. 14) in 322
northwestern China was employed to investigate the building scale within which the transformation error of F-Trans 323
is acceptable. Fifteen reference points were selected from the project (Fig. 15). The WGS-84 plane coordinates and 324
the BIM project coordinates of these reference points are listed in Table 3. The distances between the #1 point and 325
the other reference points are also listed in Table3, which range from 1.73 km to 20.76km. 326
Then, point #1 and one of the other points form a calculation set, which is used to calculate the transformation 327
parameters in Eq. (1). The points within the distance range of this calculation set form a test set. Fig. 16 shows the 328
average value of absolute errors within different ranges of 13 different calculation sets. As shown by the figure, the 329
error between Xbim (Ybim) and Xbim’ (Ybim’) increases with the increment of the distance between the reference points 330
(i.e., the project scale), and the error between Ybim and Ybim’ is nearly 2-3 times larger than the error between Xbim 331
and Xbim’. It is within the range of 7.56 km that the precision of F-Trans maintains at a submeter level, so it is 332
recommended to use F-Trans when the scale of a project is within 7 km. 333
Influencing factors of the UAV-BIM matching results 334
Statistical data of the UAV-BIM matching results shows that the average value of IoU is 74.10%, the maximum 335
value is 84.08% at 100s, and the minimum value is 52.13% at 75 s. The minimum value occurred at the moment 336
when the camera view angle changed around the building corner. This might be caused by the manual adjustment 337
of the camera attitude, during which the angular speed of the camera was changed erratically. Additionally, the 338
roaming time from the previous BIM camera state to the next one is fixed, e.g. 2000ms, and the roaming action is 339

17
continuous, during which the small change caused by manual operation would not be reflected. The above two 340
factors, i.e. the sudden adjustment of camera state and the time interval for roaming, resulted in the low IoU values. 341
Smaller roaming time interval between two adjacent statuses of the BIM camera has the potential to solve this issue, 342
but the efficiency of computation and data transition should also be considered. 343
CONCLUSIONS 344
To expedite the visual inspection of buildings, this paper presented an AR solution by the integration of BIM 345
and UAV. This innovative integration allows seamless information retrieval from BIM to augment the aerial video 346
captured by an inspection UAV. The overall workflow of the proposed algorithm was illustrated, which includes 347
coordinate transformation by F-trans, camera parameters matching, and connective animation between the aerial 348
video and BIM. Based on the aforementioned method, an AR system prototype was developed to demonstrate the 349
specific ways to support the UAV-based visual inspection for buildings. Experiments have been carried out for 350
validation purposes. The F-Trans algorithm was verified by achieving a sub-meter transformation precision. The 351
matching accuracy between the simultaneously navigating aerial video and the BIM model was quantitatively 352
evaluated, of which the IoU was 74.10%. The effectiveness of the developed AR system prototype has also been 353
demonstrated, which has great potential to enable a more efficient, comprehensive, and unbiased UAV-based 354
building inspection. 355
DATA AVAILABILITY STATEMENTS 356
Select data and code generated during the study are available from the corresponding author by request, i.e., 357
flight status data and aerial videos used in this study, and code developed to implement the proposed algorithm. 358
Some models are confidential in nature and may only be provided with restrictions as defined within the 359
department's approval for data collection, e.g., the BIM models used in this study. 360

18
ACKNOWLEDGEMENT 361
This research was supported by the National Key Research and Development Program of China (No. 362
2017YFC0405105, No. 2018YFC0406903) and the Innovative Research Groups of the National Natural Science 363
Foundation of China (No.51621092). The authors acknowledge the support of China Scholarship Council (CSC) 364
and thank Dr. Bingye Han from Beijing University of Civil Engineering and Architecture for his technical support. 365
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470

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TABLE 1. Coordinates of reference points in the calculation set 471
No.
WGS-84 (Geographic
coordinates) WGS-84 (Plane coordinates) BIM project coordinates
Longitude (°) Latitude (°) X (m) Y (m) Xbim (m) Ybim (m)
#1 -83.927833 35.955944 777193.473 3985020.21 -72.365555 30.072178
#2 -83.92802 35.955886 777176.7983 3985013.24 -70.777151 47.996734
TABLE 2. F-Trans algorithm precision analysis 472
No. Ground truth Calculated value Error
Xbim (m) Ybim (m) Xbim’ (m) Ybim’ (m) (Xbim-Xbim’)(m) (Ybim-Ybim’)(m)
#3 1.999 45.588 1.906 46.342 -0.092 0.754
#4 -50.973 -20.770 -50.630 -20.694 0.343 0.076
#5 -82.600 78.264 -83.609 78.542 -1.009 0.279
#6 -38.417 77.159 -39.071 77.648 -0.654 0.488
#7 -56.760 -34.284 -56.500 -34.009 0.260 0.275
#8 1.299 28.230 1.256 28.777 -0.043 0.548
Average error (m) -0.20 0.40
TABLE 3. Coordinates of reference points in a long-distance water diversion project 473
No.
WGS-84 (Plane coordinates) BIM project coordinates
Distance(km)
X (m) Y (m) Xbim (m) Ybim (m)
#1 595423.8 5217119 8998.991 6933.071 -
#2 593932.2 5216239 7502.195 6062.23 1.73
#3 591783.8 5215723 5348.41 5565.786 3.90
#4 591309.2 5215680 4873.852 5524.557 4.36
#5 588707.3 5215830 2279.112 5662.401 6.84
#6 588030.7 5215557 1602.587 5387.102 7.56
#7 586954.7 5213586 517.1564 3431.321 9.18
#8 583139.2 5213393 -3287.83 3214.373 12.84
#9 582750 5212772 -3677.84 2595.26 13.40
#10 580473.3 5210889 -5958.04 719.332 16.20
#11 579362.4 5210973 -7076.23 823.8809 17.20
#12 578648.9 5209644 -7792.94 -502.691 18.36
#13 578731.1 5204959 -7717.47 -5193.5 20.65
#14 579116.7 5204274 -7316.79 -5900.74 20.76
#15 581114 5203040 -5320.11 -7136.04 20.07
474
475

24
Fig. 1. Overall pipeline of UAV-BIM connective animation 476
Fig. 2. F-Trans algorithm procedure 477
Fig. 3. Virtual camera parameters: (a) Physical parameters; (b) Optical parameters 478
Fig. 4. Flowchart of connective animation 479
Fig. 5. The architecture of the AR system prototype 480
Fig. 6. Technical framework for the development of the AR system prototype 481
Fig. 7. BIM model of the Student union at the University of Tennessee, Knoxville 482
Fig. 8. Eight selected reference points shown on Google Map 483
Fig. 9. Results of BIM-UAV connective animation 484
Fig. 10. Quantitative evaluation of matching accuracy based on IoU 485
Fig. 11. Interface of the AR system prototype – Inspection specification 486
Fig. 12. Interface of the AR system prototype – Safety issues screenshots 487
Fig.13. Interface of the AR system prototype – Inspection reports 488
Fig. 14. The BIM model of a long-distance water diversion project 489
Fig. 15. Fifteen selected reference points along with the water diversion project 490
Fig. 16. Average calculation errors under different distance ranges 491
492

BIM modelBIM model
Camera parameters
matching
Camera parameters
matching
Aerial videoAerial video
Flight
status
UAV inspect
Data recordingData recording
Aerial
video
Real
Virtual
Coordinate
transformation
Coordinate
transformation
Connective
animation
Connective
animation
Position, posture,
field of view, etc.

WGS-84
(Geographic coordin ates)
WGS-84
(Plane coordinates)
Step 1: Coordinate projection
BIM project
coordinates
Step 2: Plane transformation
(,)lon lat
(,)xy
Centra l
meri dian
Equator
X
Y
O
X
Y
O
X
BIM
Y
BIM
O’
(,)
bim bim
xy
(,)XYΔΔ
ω

eye
p
m
up
v
Virtual c amera
ϕ
at
v
Look-at point

EndStart
Is video being
played?
Global sampling
time:
Retrieve data at
ddd :dddddd
F-trans and
matching algorithm
BIM camera
roaming to dddd
Is stop event
activated?
BIM target camera
status dd
Yes
No
No
Yes
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tt t=+
2tsΔ=
0tsΔ=
tt+Δ
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Database server Web browser
Network connect
• BIM model
• Aerial video and flight record
• Inspection rep orts and
spec ifications
• Algorithm parameters
• A R in spec tion
• Qu ery
• Upload (BIM, aerial video, flight record,
and et c.)
• Setting (Algor ithm parameters)

Database Server
Aerial videos Lightweight BIM
Autodesk
Forge
Viewer
Three.js
Html Page
Viewer3D.js
Tween.js
Connective
animation
Web Browser
BIM displaying
and interaction
Algorithm
parameters
Flight
record
Inspection
Specifications
Aliplayer
Video
playback
t+Δt
Data upload
or review
Ajax Json
Inspection specifications
Inspection reports
Parameters setting
Middle layer
Ftma.js
Video
upload
Model Derivative
BIM upload
Inspection
reports
Flight record
Data

Map data ©
2
2
019 Goo
gl
l
e

0 s 5 s 10 s 15 s 25 s
30 s 35 s 45 s 55 s
60 s 65 s 70 s 75 s 85 s
90 s 95 s 105 s 110 s
20 s
50s
40 s
80 s
100 s 115 s

0 s 5 s 10 s 15 s 25 s
30 s 35 s 45 s 55 s
60 s 65 s 70 s 75 s 85 s
90 s 95 s 105 s 110 s
20 s
50s
40 s
80 s
100 s 115 s
IoU:67.25% IoU:67.24% IoU:62.5 2% IoU:67.84% IoU:71.79%
IoU:70.39%
IoU:69.18% IoU:70.27% IoU:73.5 5% IoU:76.55% IoU:83.91%
IoU:77.66%
IoU:82.28% IoU:59.09% IoU:72.1 6% IoU:52.13% IoU:79.95%
IoU:82.20%
IoU:76.91% IoU:79.61% IoU:84.0 8% IoU:83.25% IoU:81.30%
IoU:79.99%

Inspection Query Uploading Setting TJU-User1
Inspection
specifications
Issues
screenshots
Inspection
reports
Major s tructure
No inclinat ion, deformation, pee ling, cracking and non-sh rinkage cracks, no expos ed steel bar, etc.
Wall space
Finish bric k stick firmly; the surf ace is smooth and clean, an d has no damage or unmatched co lor.
Roof
No agin g phenomenon o r cracks, no water accumula tion.
Door and window
Firm, flat , beautiful, and tight, no rust or access ories missing.
BIM model
Aerial video
Distance
meas uremen t
Element
properties
DJ-0374.MP4 DJ-0375.MP4
DJ-0376.MP4 DJ-0377.MP4
DJ-0378.MP4

Inspection Query Uploading Setting TJU-User1
Inspection
specifications
Issues
screenshots
Inspection
reports
BIM model
Aerial vide o
*Example
1_ Windows b roken 2_ Roof water accumulation 3_ Wall space strange color
Take screenshots
2_ Wall space strange color
×
*Example
SUBMIT

Inspection Query Uploading Setting TJU-User1
Inspection
specifications
Issues
screenshots
Inspection
reports
BIM mo del
Aerial vide o
*Example
2_ Wall space strange color
Previous p age Subm it
Issue ID
4
Issue category Is sue detail Element id Lo cation
Roof Water accumulation on the
roof of east win g 56 43679 35.9576080812 21906，
83.9289603 3843202
Condition asse ssment:
Soluti on:
4 of 4
No hazard in theory consider ing the compressive strength a nd impermeability of
the roof. Bu t intake of the drainpipe is fo und plugged up by someth ing.
Check and clean th e i ntake of the drainpipe.

BIM model
Satellite Image © 2019 Maxar Techno logies

Google Earth
Image © 2019 Maxar Technologies

0
1
2
3
4
5
6
7
0 2 4 6 8 10 12 14 16 18 20 22
Average error/m
Distanc e/km
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∣Ybim-Ybim'∣
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bim
-X
bim
’
|Y
bim
-Y
bim
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|
|