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To overcome the problem of the low accuracy and large accumulated errors of indoor mobile navigation and positioning, a method to integrate the light detection and ranging- and inertial measurement unit-based measurement is proposed. Firstly, the voxel-scale-invariant feature transform feature extraction algorithm for light detection and ranging is...
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... Some researchers have integrated laser SLAM and visual SLAM to solve geometrically similar environmental problems, ground material problems, and global localization problems [26,[79][80][81][82]. Visual-semantic SLAM maps are built based on laser SLAM maps [83,84]. There is also fusion with other localization methods: odometer [85], Wi-Fi [86,87], IMU [29,30], encoder, RTK, IMU, and UWB [36]. ...
Recently, with the in-depth development of Industry 4.0 worldwide, mobile robots have become a research hotspot. Indoor localization has become a key component in many fields and the basis for all actions of mobile robots. This paper screened 147 papers in the field of indoor positioning of mobile robots from 2019 to 2021. First, 12 mainstream indoor positioning methods and related positioning technologies for mobile robots are introduced and compared in detail. Then, the selected papers were summarized. The common attributes and laws were discovered. The development trend of indoor positioning of mobile robots is obtained.
... Yan et al. found that a combination of light detection systems with IMU data for mobile robot localization provide a higher precision compared to the individual sensor elements [26]. Ibrahim et al. investigated the determination and tracking of human activity through VLS technologies with sensor elements placed on the floor or worn by the person being studied [27]. ...
The rapid development of microsystems technology with the availability of various machine learning algorithms facilitates human activity recognition (HAR) and localization by low-cost and low-complexity systems in various applications related to industry 4.0, healthcare, ambient assisted living as well as tracking and navigation tasks. Previous work, which provided a spatiotemporal framework for HAR by fusing sensor data generated from an inertial measurement unit (IMU) with data obtained by an RGB photodiode for visible light sensing (VLS), already demonstrated promising results for real-time HAR and room identification. Based on these results, we extended the system by applying feature extraction methods of the time and frequency domain to improve considerably the correct determination of common human activities in industrial scenarios in combination with room localization. This increases the correct detection of activities to over 90% accuracy. Furthermore, it is demonstrated that this solution is applicable to real-world operating conditions in ambient light.
... A method to accurately locate persons indoors by fusing INS with active RFID was presented in [19]. A method to integrate LiDAR and IMU was proposed to overcome the problem of the low accuracy and large accumulated errors of indoor mobile navigation and positioning [20]. In order to improve the accuracy of the data fusion filter, a tightly coupled UWB/INS-integrated scheme for indoor human navigation was investigated [21]. ...
Since there are many interferences in the indoor environment, it is difficult to achieve the precise positioning of the mobile robot using a single sensor. This paper presents a position estimation and positioning error correction method of mobile robots based on multisensor data. The robot’s positioning sensor includes ultra-wideband (UWB) components, inertial measurement unit (IMU), and encoders. UWB multipath interference causes more ranging errors, which can be reduced by the correction equation after data fitting. The real-time coordinates of the UWB robot tag can be calculated based on multiple UWB anchor data and the least squares method. The coordinate data x c , y c are acquired by UWB positioning subsystem, and the velocity data x ̇ c , y ̇ c are collected by IMU together with encoders. The multisensor data continuously update Kalman filter and estimate robot position. In the positioning process, the positioning data of different sensors can be mutually corrected and supplemented. The results of UWB ranging correction experiments indicate that data fitting can improve the UWB positioning accuracy. In the multisensor positioning experiments, compared with a single sensor, the positioning method based on data fusion of UWB, IMU, and encoders has higher accuracy and adaptability. When UWB signals are interfered or invalid, other sensors can still work normally and complete the robot positioning process. The multisensor positioning method not only improves the robot positioning accuracy but also has stronger environmental adaptability.
... Combining several different navigation systems together can make use of multiple information sources to complement each other for the purpose of forming a multidimensional, multifunctional, and high-accuracy navigation system. At present, the mainstream integration methods are Lidar/ IMU, 14,15 Lidar/Visual, 16 Visual/IMU, 17 Lidar/Visual/ IMU, 18 and so on. ...
... And the attitude information collected by MTi is used to compensate for the posture error caused by the alone Lidar scan matching through RKF, which can improve the positioning accuracy of the direction angle, and also improve the stability of the integrated system. In addition, by adding IMU sensing information, the cumulative errors 14,18 in the process of Lidar data processing are compensated and updated in real time, the fusion algorithm can better perform cumulative errors, it can be seen from Figure 6(c) that the last position (7.5032, 2.9110) the robot returns (the end point) to is very close to the starting point (7.4533, 2.9492). Further quantitative analysis is shown in Table 3. Table 3 shows the error statistics in the X and Y directions with the three algorithms for indoor positioning, where STD is the standard deviation, MAX is the maximum error value, and mean is the average error. ...
As an important research field of mobile robot, simultaneous localization and mapping technology is the core technology to realize intelligent autonomous mobile robot. Aiming at the problems of low positioning accuracy of Lidar (light detection and ranging) simultaneous localization and mapping with nonlinear and non-Gaussian noise characteristics, this article presents a mobile robot simultaneous localization and mapping method that combines Lidar and inertial measurement unit to set up a multi-sensor integrated system and uses a rank Kalman filtering to estimate the robot motion trajectory through inertial measurement unit and Lidar observations. Rank Kalman filtering is similar to the Gaussian deterministic point sampling filtering algorithm in structure, but it does not need to meet the assumptions of Gaussian distribution. It completely calculates the sampling points and the sampling points weights based on the correlation principle of rank statistics. It is suitable for nonlinear and non-Gaussian systems. With multiple experimental tests of small-scale arc trajectories, we can see that compared with the alone Lidar simultaneous localization and mapping algorithm, the new algorithm reduces the mean error of the indoor mobile robot in the X direction from 0.0928 m to 0.0451 m, with an improved accuracy rate of 46.39%, and the mean error in the Y direction from 0.0772 m to 0.0405 m, which improves the accuracy rate of 48.40%. Compared with the extended Kalman filter fusion algorithm, the new algorithm reduces the mean error of the indoor mobile robot in the X direction from 0.0597 m to 0.0451 m, with an improved accuracy rate of 24.46%, and the mean error in the Y direction from 0.0537 m to 0.0405 m, which improves the accuracy rate of 24.58%. Finally, we also tested on a large-scale rectangular trajectory, compared with the extended Kalman filter algorithm, rank Kalman filtering improves the accuracy of 23.84% and 25.26% in the X and Y directions, respectively, it is verified that the accuracy of the algorithm proposed in this article has been improved.
... The approach was used in the hector simultaneous localization and mapping (SLAM) scheme to estimate the robot pose [6]. Onother LiDAR/inertial measurement unit(IMU)-integrated navigation positioning scheme for indoor mobile robots has been designed in [20]. ...
In order to overcome the uncertainty of the data sampling period of the sensor due to equipment reasons, a mobile robot localization system is developed under the uncertain sampling period for the tightly-fused light detection and ranging (LiDAR), compass, and encoder data. The errors of position and velocity, the robot’s yaw, and the sampling period are chosen as state variables. The ranges between the corner feature points (CFPs) and the mobile robot measured by the LiDAR, compass, and encoder are considered as an observation. Based on the tightly-integrated nonlinear model, the extended unbiased finite-impulse response (EFIR) filter fuses the sensors’ data for the integrated localization system. The performances of the traditional loosely-coupled integration scheme, tightly-coupled integration scheme with a constant sampling interval, and tightly-coupled integration with an uncertain sampling interval are compared based on real data. It is shown experimentally that the proposed scheme is more accurate then the traditional loosely-coupled integration and the one relying on a constant sampling interval, which improves by about 10.2%.
A aplicação das tecnologias e conceitos da Indústria 4.0 nos processos logísticos industriais é uma estratégia eficiente para reduzir custos operacionais, agilizar as entregas e aumentar a eficiência do empreendimento. Assim, este trabalho tem como objetivo a realização do projeto conceitual de um sistema logístico automatizado envolvendo a movimentação, armazenagem e gerenciamento dos produtos embalados de uma indústria moveleira. A elaboração deste projeto utilizou as duas primeiras etapas da metodologia de desenvolvimento de projeto proposta por Pahl, Beitz, Feldhusem e Grote (2005), a fase de planejamento e a fase de concepção. Estas etapas permitiram elaborar a lista de requisitos, obter e avaliar as variantes de solução e apresentar o projeto conceitual de um sistema logístico automatizado baseado nas tecnologias da Indústria 4.0 e Logística 4.0 através da identificação dos produtos por radiofrequência e da movimentação por robôs móveis autônomos. Visando a fabricação e implantação deste projeto conceitual são necessárias mais duas etapas da metodologia de Pahl et al. (2005), a fase do projeto preliminar e a fase do projeto detalhado. Estas etapas definirão as especificações técnicas dos equipamentos e o detalhamento da forma de funcionamento dos transportadores, do sistema de armazenagem e do software de gerenciamento, culminando com o layout detalhado do sistema logístico automatizado. Assim, o projeto possui potencial de alavancar o crescimento da indústria moveleira, pois proporcionará um aumento da capacidade de armazenagem e transporte dos produtos de maneira independente da atuação humana e garantirá a rastreabilidade dos produtos em tempo real.
When the global position system (GPS) signal is unavailable, the performance of the GPS/ inertial navigation system (INS) integrated navigation system degrades severely. In this article, the performance of the ultra-low cost inertial measurement unit (IMU) is studied and the objective is to enhance its performance during GPS outages. To be specific, a performance compensation method is proposed, which consists of two parts. First, to deal with the large noise and drift of the micro-electro-mechanical system (MEMS)-based inertial measurement unit (IMU), a wavelet regional correlation threshold denoising algorithm is proposed. Then, to improve the performance of traditional LSTM network when dealing with navigation data with strong coupling, a convolutional neural network-long short-term memory (CNN-LSTM) model is formulated. It employs CNN to quickly extract the features of the input, and utilizes LSTM network to output pseudo-GPS signals as the compensation object. Finally, simulation experiments and real road tests are implemented to evaluate the proposed method. Comparison experiment results show that the proposed method can effectively improve the performance of the integrated navigation system during GPS outages.
