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Invariant Extended Kalman Filtering for Tightly Coupled LiDAR-Inertial Odometry and Mapping

文献类型：期刊论文

作者Shi, Pengcheng2,3; Zhu, Zhikai1; Sun, Shiying3; Zhao, Xiaoguang3; Tan, Min2,3
刊名IEEE-ASME TRANSACTIONS ON MECHATRONICS
出版日期2023-01-10
页码12
关键词Laser radar Feature extraction Simultaneous localization and mapping Robustness Point cloud compression Optimization Kalman filters Invariant extended kalman filter (EKF) light detection and ranging (LiDAR)-inertial odometry multisensor fusion localization state estimation
ISSN号1083-4435
DOI10.1109/TMECH.2022.3233363
通讯作者Sun, Shiying(sunshiying2013@ia.ac.cn)
英文摘要In this article, we extend the invariant extended Kalman filter (EKF) to light detection and ranging (LiDAR)-inertial odometry and mapping systems using invariant observer design and the theory of Lie groups for directly fusing LiDAR and inertial measurement unit (IMU) measurements. We consider this from two different aspects and implement two independent systems. Specifically, we propose a robo-centric invariant EKF LiDAR-inertial odometry termed Inv-LIO1. Its mapping module is an ordinary used one and two modules run in separate threads. A world-centric invariant EKF LiDAR-inertial odometry termed Inv-LIO2 is designed and implemented, which has an integrated odometry and mapping architecture. In Inv-LIO1, the output of the filter is the pose estimated by the scan-to-scan match method, which serves as the initial estimate of the mapping module that refines the odometry and constructs a 3-D map. The robo-centric formulation represents that the state in a local frame shifted at every LiDAR time to prevent filter divergence. Inv-LIO2 directly fuses LiDAR feature points and IMU data to obtain the map-refined odometry by scan-to-map match method, followed by global map update. To validate the effectiveness and robustness of the proposed method, we conduct extensive experiments in various indoor and outdoor environments. Overall, Inv-LIO1 offers pure odometry with higher accuracy than other state-of-the-art systems, improving the overall performance. Inv-LIO2 achieves superior accuracy over other state-of-the-art systems in the map-refined odometry comparison.
WOS关键词ROBUST
资助项目National Natural Science Foundation of China[62203438] ; National Natural Science Foundation of China[62103410] ; National Key Research and Development Project of China[2021ZD0140409] ; National Key Research and Development Project of China[2019YFB1310601] ; Science and Technology Project of Beijing[Z221100000222015] ; Science and Technology Project of Beijing[Z211100004021020]
WOS研究方向Automation & Control Systems ; Engineering
语种英语
WOS记录号WOS:000915490000001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; National Key Research and Development Project of China ; Science and Technology Project of Beijing
源URL[http://ir.ia.ac.cn/handle/173211/51416]  
专题智能机器人系统研究
通讯作者Sun, Shiying
作者单位1.NIO Inc, Shanghai 201804, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Shi, Pengcheng,Zhu, Zhikai,Sun, Shiying,et al. Invariant Extended Kalman Filtering for Tightly Coupled LiDAR-Inertial Odometry and Mapping[J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS,2023:12.
APA Shi, Pengcheng,Zhu, Zhikai,Sun, Shiying,Zhao, Xiaoguang,&Tan, Min.(2023).Invariant Extended Kalman Filtering for Tightly Coupled LiDAR-Inertial Odometry and Mapping.IEEE-ASME TRANSACTIONS ON MECHATRONICS,12.
MLA Shi, Pengcheng,et al."Invariant Extended Kalman Filtering for Tightly Coupled LiDAR-Inertial Odometry and Mapping".IEEE-ASME TRANSACTIONS ON MECHATRONICS (2023):12.

入库方式： OAI收割

来源：自动化研究所

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