Global localization on LiDAR point cloud maps is a challenging task because of the sparse nature of point clouds and the large size difference between LiDAR scans and the maps. In this paper, we solve the LiDAR-based global localization problem based upon the plane-motion assumption. We first project the clouds into Bird’s-eye View (BV) images and transform the problem into a BV image matching problem. We then introduce a novel local descriptor, i.e., Histogram of Orientations of Principal Normals (HOPN), to perform matching. The HOPN descriptor encodes the point normals of the clouds, and is more effective in matching BV images than the common image descriptors. In addition, we present the consensus set maximization algorithm to robustly estimate a rigid pose from the HOPN matches in the case of the low inlier ratio. The experimental results on three large-scale datasets show that our method achieves state-of-the-art global localization performance when using either single LiDAR scans or local maps.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    LiDAR-Based Global Localization Using Histogram of Orientations of Principal Normals


    Contributors:
    Luo, Lun (author) / Cao, Si-Yuan (author) / Sheng, Zehua (author) / Shen, Hui-Liang (author)

    Published in:

    Publication date :

    2022-09-01


    Size :

    8372975 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    3D LiDAR-Based Global Localization Using Siamese Neural Network

    Yin, Huan / Wang, Yue / Ding, Xiaqing et al. | IEEE | 2020



    LiDAR-based Cooperative Relative Localization

    Dong, Jiqian / Chen, Qi / Qu, Deyuan et al. | IEEE | 2023



    Shapelets Correlated with Surface Normals Produce Surfaces

    Kovesi, P. / IEEE | British Library Conference Proceedings | 2005