This paper presents an online static probability map and odometry estimation using automotive LiDAR for urban autonomous driving. In an urban autonomous driving environment where high-rise buildings and many vehicles exist, estimation of vehicle odometry is an important factor for perception and localization accuracy. Therefore, this study aims to increase the accuracy of vehicle odometry in vulnerable GPS situations. The algorithm consists of two LiDAR point cloud processing modules. The first module constructs static probability map (SPM) and estimates the vehicle odometry using correspondence between consecutive SPM. The second module tracks moving objects using a particle filter based geometric model-free approach (GMFA-PF). Two modules operating in parallel enable online operation by using mutual results for preprocessing. The proposed algorithm has been implemented in the robot operating system(ROS) environment and investigated via actual urban driving. Test results show that the online LiDAR pointwise estimation algorithm improves the vehicle odometry estimation and moving object tracking performance compared with the previous extended kalman filter based method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Online Static Probability Map and Odometry Estimation using Automotive LiDAR for Urban Autonomous Driving


    Contributors:
    Kim, Jongho (author) / Lee, Hojoon (author) / Yi, Kyongsu (author)


    Publication date :

    2021-09-19


    Size :

    1406279 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Self-Supervised Depth Completion From Direct Visual-LiDAR Odometry in Autonomous Driving

    Song, Zhenbo / Lu, Jianfeng / Yao, Yazhou et al. | IEEE | 2022


    DELIO: DECOUPLED LIDAR ODOMETRY

    Thomas, Queens Maria / Wasenmüller, Oliver / Stricker, Didier | British Library Conference Proceedings | 2019


    Pose Estimation with Lidar Odometry and Cellular Pseudoranges

    Kassas, Zaher / Khalife, Joe / Ragothaman, Sonya | British Library Conference Proceedings | 2017


    Visual Odometry Integrated Semantic Constraints towards Autonomous Driving

    Lan, FengChong / Chen, Jiqing / Yao, Siyu | SAE Technical Papers | 2022


    Dynamic Object-Aware LiDAR Odometry Aided by Joint Weightings Estimation in Urban Areas

    Huang, Feng / Wen, Weisong / Zhang, Jiachen et al. | IEEE | 2024