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GR-LOAM: LiDAR-based sensor fusion SLAM for ground robots on complex terrain
Su Y(苏赟)1,2,3; Wang T(王挺)1,2; Shao SL(邵士亮)1,2; Yao C(姚辰)1,2; Wang ZD(王志东)4
Department机器人学研究室
Source PublicationRobotics and Autonomous Systems
ISSN0921-8890
2021
Volume140Pages:1-13
Indexed BySCI ; EI
EI Accession number20211010021943
WOS IDWOS:000642480300006
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China (U20A20201) ; LiaoNing Revitalization Talents Program, China (XLYC1807018).
KeywordSimultaneous localization and mapping (SLAM) Ground robot Encoder Sensor fusion Tight coupling scheme
Abstract

Simultaneous localization and mapping is a fundamental process in robot navigation. We focus on LiDAR to complete this process in ground robots traveling on complex terrain by proposing GR-LOAM, a method to estimate robot ego-motion by fusing LiDAR, inertial measurement unit (IMU), and encoder measurements in a tightly coupled scheme. First, we derive a odometer increment model that fuses the IMU and encoder measurements to estimate the robot pose variation on a manifold. Then, we apply point cloud segmentation and feature extraction to obtain distinctive edge and planar features. Moreover, we propose an evaluation algorithm for the sensor measurements to detect abnormal data and reduce their corresponding weight during optimization. By jointly optimizing the cost derived from the LiDAR, IMU, and encoder measurements in a local window, we obtain low-drift odometry even on complex terrain. We use the estimated relative pose in the local window to reevaluate the matching distance across features and remove dynamic objects and outliers, thus refining the features before being fed to a mapping thread and increasing the mapping efficiency. In the back end, GR-LOAM uses the refined point cloud and tightly couples the IMU and encoder measurements with ground constraints to further refine the estimated pose by aligning the features on a global map. Results from extensive experiments performed in indoor and outdoor environments using real ground robot demonstrate the high accuracy and robustness of the proposed GR-LOAM for state estimation of ground robots.

Language英语
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Robotics
WOS KeywordVERSATILE ; ODOMETRY ; ROBUST
WOS Research AreaAutomation & Control Systems ; Computer Science ; Robotics
Funding ProjectNational Natural Science Foundation of China[U20A20201] ; LiaoNing Revitalization Talents Program, China[XLYC1807018]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28412
Collection机器人学研究室
Corresponding AuthorSu Y(苏赟); Wang T(王挺)
Affiliation1.The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Department of Advanced Robotics, Chiba Institute of Technology, Chiba, Japan
Recommended Citation
GB/T 7714
Su Y,Wang T,Shao SL,et al. GR-LOAM: LiDAR-based sensor fusion SLAM for ground robots on complex terrain[J]. Robotics and Autonomous Systems,2021,140:1-13.
APA Su Y,Wang T,Shao SL,Yao C,&Wang ZD.(2021).GR-LOAM: LiDAR-based sensor fusion SLAM for ground robots on complex terrain.Robotics and Autonomous Systems,140,1-13.
MLA Su Y,et al."GR-LOAM: LiDAR-based sensor fusion SLAM for ground robots on complex terrain".Robotics and Autonomous Systems 140(2021):1-13.
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