Asynchronous Multiple LiDAR-Inertial Odometry Using Point-Wise Inter-LiDAR Uncertainty Propagation

被引:5
|
作者
Jung, Minwoo [1 ]
Jung, Sangwoo [1 ]
Kim, Ayoung [1 ]
机构
[1] SNU, Dept Mech Engn, Seoul 08826, South Korea
来源
关键词
Range Sensing; SLAM; Mapping;
D O I
10.1109/LRA.2023.3281264
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
In recent years, multiple Light Detection and Ranging (LiDAR) systems have grown in popularity due to their enhanced accuracy and stability from the increased field of view (FOV). However, integrating multiple LiDARs can be challenging, attributable to temporal and spatial discrepancies. Common practice is to transform points among sensors while requiring strict time synchronization or approximating transformation among sensor frames. Unlike existing methods, we elaborate the inter-sensor transformation using continuous-time (CT) inertial measurement unit (IMU) modeling and derive associated ambiguity as a point-wise uncertainty. This uncertainty, modeled by combining the state covariance with the acquisition time and point range, allows us to alleviate the strict time synchronization and to overcome FOV difference. The proposed method has been validated on both public and our datasets and is compatible with various LiDAR manufacturers and scanning patterns.
引用
收藏
页码:4211 / 4218
页数:8
相关论文
共 13 条
  • [1] A LiDAR-inertial Odometry with Principled Uncertainty Modeling
    Jiang, Binqian
    Shen, Shaojie
    2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2022, : 13292 - 13299
  • [2] LOG-LIO2: A LiDAR-Inertial Odometry With Efficient Uncertainty Analysis
    Huang, Kai
    Zhao, Junqiao
    Lin, Jiaye
    Zhu, Zhongyang
    Song, Shuangfu
    Ye, Chen
    Feng, Tiantian
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2024, 9 (10): : 8226 - 8233
  • [3] Section-LIO: A High Accuracy LiDAR-Inertial Odometry Using Undistorted Sectional Point
    Meng, Kai
    Sun, Hui
    Qi, Jiangtao
    Wang, Hongbo
    IEEE ACCESS, 2023, 11 : 144918 - 144927
  • [4] Asynchronous State Estimation of Simultaneous Ego-motion Estimation and Multiple Object Tracking for LiDAR-Inertial Odometry
    Lin, Yu-Kai
    Lin, Wen-Chieh
    Wang, Chieh-Chih
    2023 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2023), 2023, : 10616 - 10622
  • [5] GPC-LIVO: Point-wise LiDAR-inertial-visual odometry with geometric and photometric composite measurement model
    Ye, Chenxi
    Nan, Bingfei
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2025, 185
  • [6] Feature-Based Scanning LiDAR-Inertial Odometry Using Factor Graph Optimization
    Setterfield, Timothy P.
    Hewitt, Robert A.
    Espinoza, Antonio Teran
    Chen, Po-Ting
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2023, 8 (06) : 3374 - 3381
  • [7] UA-LIO: An Uncertainty-Aware LiDAR-Inertial Odometry for Autonomous Driving in Urban Environments
    Wu, Qi
    Chen, Xieyuanli
    Xu, Xiangyu
    Zhong, Xinliang
    Qu, Xingwei
    Xia, Songpengcheng
    Liu, Guoqing
    Liu, Liu
    Yu, Wenxian
    Pei, Ling
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2025, 74
  • [8] Continuous-Time Radar-Inertial and Lidar-Inertial Odometry Using a Gaussian Process Motion Prior
    Burnett, Keenan
    Schoellig, Angela P.
    Barfoot, Timothy D.
    IEEE TRANSACTIONS ON ROBOTICS, 2025, 41 : 1059 - 1076
  • [9] Need for Speed: Fast Correspondence-Free Lidar-Inertial Odometry Using Doppler Velocity
    Yoon, David J.
    Burnett, Keenan
    Laconte, Johann
    Chen, Yi
    Vhavle, Heethesh
    Kammel, Soeren
    Reuther, James
    Barfoot, Timothy D.
    2023 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2023, : 5304 - 5311
  • [10] Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial odometry Using Parallel Sparse Incremental Voxels
    Bai, Chunge
    Xiao, Tao
    Chen, Yajie
    Wang, Haoqian
    Zhang, Fang
    Gao, Xiang
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 7 (02): : 4861 - 4868