Abstract
When it comes to optimal dynamic localization, high accuracy and robustness localization is the main challenge for the autonomous mobile robot. In this paper, an optimal dynamic localization framework with integrating sensors fusion is considered. The global point map is utilized to provide absolute pose observation information, and the multi-sensor information is applied to realize robust localization in complex outdoor environments. The multi-sensor technique, including 3D-Lidar, global positioning system (GPS), and inertial measurement unit (IMU), is adopted to construct the global point map by pose optimization so that the absolute position and attitude observation information can still be provided when the outdoor GPS signal fails. Meanwhile, in the case of optimal localization, the system kinematics equation is constructed by the IMU error model, and the map pose is matched by map scanning. Moreover, the GPS position information participates in multi-source fusion when the GPS signal is reliable. Finally, the experimental results show that the average localization error is within 0.05 meters, reflecting the flexibility of dynamic localization.
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This work was supported by the National Key Research and Development Program of China under Grant 2019YFC1511401, and the National Natural Science Foundation of China under Grant 62173038 and 62203176.
Jing Li was born in 1982. She received her M.S. degree of engineering from Shandong University of Technology in 2007, and a Ph.D. degree in control science and engineering from Beijing Institute of Technology in 2011. She is now an associate professor of School of Automation, State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology. Her research interests include image detection technology, and object detection and tracking.
Keyan Guo was born in 1997. He received his B.S. degree in automation from Ocean University of China, Qingdao, China, in 2019. He is currently pursuing an M.S. degree as a member of State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, China. His current research interests include simultaneous localization and mapping of mobile robot.
Junzheng Wang received his Ph.D. degree in control science and engineering from the Beijing Institute of Technology, Beijing, China, in 1994. He is the Deputy Director with the State Key Laboratory of Intelligent Control and Decision of Complex Systems, the Director of the Key Laboratory of Servo Motion System Drive and Control, and the Dean of the Graduate School of Beijing Institute of Technology, where he is a Professor and a Ph.D. Supervisor. His current research interests include motion control, electric hydraulic servo system, and dynamic target detection and tracking based on image technology. He received the Second Award from the National Scientific and Technological Progress (No.1) of China.
Jiehao Li received his M.Sc. degree in control engineering at South China University of Technology, Guangzhou, China, in 2017. He received a Ph.D. degree at the State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, China, in 2022. He is now an Associate Professor at College of Engineering, South China Agricultural University, Guangzhou, China. He is also a Visiting Fellow of the Medical and Robotic Surgery Group (NEARLab) in Politecnico di Milano, Milano, Italy. His research interests mainly include mobile robotics, motion control, robot vision, and image processing. Prof. Li is the Academic Committee Member of Youth Working Committee of CAAI and CICC. He has been awarded the Best Conference Paper Finalist of IEEE ICARM2020, the Outstanding Reviewer of CAC2021, and the Outstanding Session Chair of WRC SARA2022. He is the Conference Session Chair of IEEE ICUS2022, ICIRA2022 and YAC2022. He has served as the Guest Editor of IET Control Theory & Applications, Frontiers in Neurorobotics, and the Associate Editor of Journal of Control Science and Engineering.
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Li, J., Guo, K., Wang, J. et al. Towards Optimal Dynamic Localization for Autonomous Mobile Robot via Integrating Sensors Fusion. Int. J. Control Autom. Syst. 21, 2648–2663 (2023). https://doi.org/10.1007/s12555-021-1088-7
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DOI: https://doi.org/10.1007/s12555-021-1088-7