Knowledge Commons of Institute of Automation，CAS
| Invariant Extended Kalman Filtering for Tightly Coupled LiDAR-Inertial Odometry and Mapping | |
Shi, Pengcheng1,2; Zhu, Zhikai3 ; Sun, Shiying1; Zhao, Xiaoguang1; Tan, Min1,2
| |
| Source Publication | IEEE-ASME TRANSACTIONS ON MECHATRONICS
 |
| ISSN | 1083-4435 |
| 2023-01-10 | |
| Pages | 12 |
| Corresponding Author | Sun, Shiying(sunshiying2013@ia.ac.cn) |
| Abstract | 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. |
| Keyword | 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 |
| DOI | 10.1109/TMECH.2022.3233363 |
| WOS Keyword | ROBUST |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | National Natural Science Foundation of China ; National Key Research and Development Project of China ; Science and Technology Project of Beijing |
| WOS Research Area | Automation & Control Systems ; Engineering |
| WOS Subject | Automation & Control Systems ; Engineering, Manufacturing ; Engineering, Electrical & Electronic ; Engineering, Mechanical |
| WOS ID | WOS:000915490000001 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/51417 |
| Collection | 多模态人工智能系统全国重点实验室 复杂系统认知与决策实验室 |
| Corresponding Author | Sun, Shiying |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.NIO Inc, Shanghai 201804, Peoples R China |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment