A LiDAR/IMU spatial calibration method based on LiDAR labels and occupancy grid map

doi:10.11947/j.AGCS.2023.20220242

Abstract

Abstract: LiDAR and inertial measurement unit (IMU) have been widely used in intelligent vehicles, such as high-precision map generation, real-time vehicle positioning, etc. When LiDAR and IMU work together, it is necessary to know the spatial relationship between the two sensors, including the spatial rotation and translation parameters. This paper proposes an automated LiDAR/IMU spatial calibration method based on LiDAR labels. We first analyze the influence of LiDAR/IMU calibration parameters on LiDAR point cloud splicing, and prove that when the vehicle moves approximately in a straight line, LiDAR points are converted to a consistent axial direction using the approximate calibration parameters of LiDAR/IMU. Based on this conclusion, a method for generating a LiDAR grid occupancy map with high relative accuracy based on IMU attitude constraint is proposed. Point clouds of LiDAR labels with high-precision global position are matched with the map to obtain positions of the labels in the map. Based on the known high-precision position of the LiDAR labels, a nonlinear optimization method is used to solve the spatial transformation relationship between the grid occupancy map and the LiDAR labels, and the spatial calibration parameters of the LiDAR/IMU is further solved. The experimental results show that the point cloud map constructed by the solved LiDAR/IMU calibration parameters can achieve absolute position accuracy of centimeter level, which verifies the feasibility of our method.

Key words: spatial calibration, LiDAR, inertial measurement unit, LiDAR labels

CLC Number:

P228

QIAN Chuang, ZHANG Hongjuan, LI Wenzhuo, LIU Hui, LI Bijun. A LiDAR/IMU spatial calibration method based on LiDAR labels and occupancy grid map[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(9): 1469-1479.

References

[1] 李帅鑫, 李广云, 王力, 等. LiDAR/IMU紧耦合的实时定位方法[J]. 自动化学报, 2021, 47(6): 1377-1389. LI Shuaixin, LI Guangyun, WANG Li, et al. LiDAR/IMU tightly coupled real-time localization method[J]. Acta Automatica Sinica, 2021, 47(6): 1377-1389.
[2] ZHANG Yi. LILO: a novel LiDAR-IMU SLAM system with loop optimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(4): 2649-2659.
[3] 孙喜亮, 关宏灿, 苏艳军, 等. 面向高精度城市测绘的激光紧耦合SLAM方法[J]. 测绘学报, 2021, 50(11): 1585-1593. DOI: 10.11947/j.AGCS.2021.20210243. SUN Xiliang, GUAN Hongcan, SU Yanjun, et al. A tightly coupled SLAM method for precise urban mapping[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(11): 1585-1593. DOI: 10.11947/j.AGCS.2021.20210243.
[4] KARAM S, LEHTOLA V, VOSSELMAN G. Strategies to integrate IMU and LiDAR SLAM for indoor mapping[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, V-1-2020: 223-230.
[5] WANG Y, LI S, WANG M, et al. A simple deep learning network for classification of 3D mobile LiDAR point clouds[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(3): 49-50.
[6] WANG X, MA H, ZHANG L, et al. Point cloud classification and accuracy analysis based on feature fusion[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(3):38-48.
[7] LI Shuaixin, WANG Li, LI Jiuren, et al. 3D LiDAR/IMU calibration based on continuous-time trajectory estimation in structured environments[J]. IEEE Access, 2021, 9: 138803-138816.
[8] 聂明炎, 杨诚. 一种LiDAR平面配准方法辅助的IMU室内定位算法[J]. 测绘地理信息, 2021, 46(5): 27-30. NIE Mingyan, YANG Cheng. An IMU indoor location algorithm assisted by LiDAR plane registration method[J]. Journal of Geomatics, 2021, 46(5): 27-30.
[9] ROSALES J J, COLOMINA I. A flexible approach for the numerical solution of the INS mechanization equations[C]//Proceedings of the 6th Geomatic Week. Barcelona:[s.n.], 2005.
[10] 宗文鹏. 基于LiDAR的移动机器人自主定位技术研究[D]. 郑州:信息工程大学, 2020. ZONG Wenpeng. Research on LiDAR based autonomous localization for mobile robots[D]. Zhengzhou: Information Engineering University, 2020.
[11] 陈雪松. 激光雷达和惯性测量紧耦合的车辆高精度定位研究[D]. 长春: 吉林大学, 2021. CHEN Xuesong. Research on high-precision localization of vehicles with tightly coupled of LiDAR and inertial measurement[D]. Changchun: Jilin University, 2021.
[12] WONG H, ZHANG Xiaolong, WEN Tuopu, et al. HD-map aided LiDAR-INS extrinsic calibration[C]//Proceedings of 2021 IEEE International Intelligent Transportation Systems Conference. Indianapolis: IEEE, 2021: 3136-3143.
[13] TAYLOR Z, NIETO J. Motion-based calibration of multimodal sensor arrays[C]//Proceedings of 2015 IEEE International Conference on Robotics and Automation. Seattle:IEEE, 2015: 4843-4850.
[14] LEE W, YANG Yulin, HUANG Guoquan. Efficient multi-sensor aided inertial navigation with online calibration[C]//Proceedings of 2021 IEEE International Conference on Robotics and Automation. Xi'an: IEEE, 2021: 5706-5712.
[15] ZUO Xingxing, YANG Yulin, GENEVA P, et al. LIC-fusion 2.0: LiDAR-inertial-camera odometry with sliding-window plane-feature tracking[C]//Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. Las Vegas: IEEE, 2020: 5112-5119.
[16] LV Jiajun, XU Jinhong, HU Kewei, et al. Targetless calibration of LiDAR-IMU system based on continuous-time batch estimation[C]//Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. Las Vegas: IEEE, 2020: 9968-9975.
[17] LE GENTIL C, VIDAL-CALLEJA T, HUANG Shoudong. 3D LiDAR-IMU calibration based on upsampled preintegrated measurements for motion distortion correction[C]//Proceedings of 2018 IEEE International Conference on Robotics and Automation. Brisbane: IEEE, 2018: 2149-2155.
[18] TAGLIABUE A, TORDESILLAS J, CAI Xiaoyi, et al. LION: LiDAR-inertial observability-aware navigator for vision-denied environments[C]//Proceedings of 2020 International Symposium on Experimental Robotics. Cham: Springer, 2020: 380-390.
[19] LIU Wanli, LI Zhixiong, MALEKIAN R, et al. A novel multifeature based on-site calibration method for LiDAR-IMU system[J]. IEEE Transactions on Industrial Electronics, 2020, 67(11): 9851-9861.
[20] 吴昱晗, 王蕴宝, 薛庆全, 等. 一种基于点云匹配的激光雷达/IMU联合标定方法[J]. 电子技术应用, 2019, 45(12): 78-82. WU Yuhan, WANG Yunbao, XUE Qingquan, et al. A LiDAR/IMU joint calibration method based on point cloud matching[J]. Application of Electronic Technique, 2019, 45(12): 78-82.
[21] LIU W, LI Y. Error modeling and extrinsic-intrinsic calibration for LiDAR-IMU system based on cone-cylinder features[J]. Robotics and Autonomous Systems, 2019, 114: 124-133.
[22] 张文涛. 多用途小型LiDAR系统集成与应用研究[D]. 焦作: 河南理工大学, 2018. ZHANG Wentao. The integration and application research of small multipurpose LiDAR system[D]. Jiaozuo: Henan Polytechnic University, 2018.
[23] MISHRA S, PANDEY G, SARIPALLI S. Target-free extrinsic calibration of a 3D-LiDAR and an IMU[C]//Proceedings of 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Karlsruhe: IEEE, 2021: 1-7.
[24] MCDERMOTT M, RIFE J. Mitigating shadows in LiDAR scan matching using spherical voxels[J]. IEEE Robotics and Automation Letters, 2022, 7(4): 12363-12370.
[25] 钱闯. 弱GNSS信号区域的GNSS/INS/LiDAR高精度定位方法及其应用研究[D]. 武汉: 武汉大学, 2017. QIAN Chuang. Research on GNSS/INS/LiDAR high precision positioning method in weak GNSS signal regionand its application[D]. Wuhan: Wuhan University, 2017.