Scene coding for multi-scale LiDAR-based localization of intelligent vehicle with Siamese network
TAO Qian-wen
HU Zhao-zheng
SUN Xun-pei
WAN Jin-jie
CHEN Qi-li
Abstract：Intelligent vehicle localization methods based on LiDAR point cloud maps have problems such as large amounts of map data and low matching accuracy,hence,a scene coding method and a multi-scale LiDAR-based localization method of intelligent vehicles based on Siamese network are proposed.Firstly,a polarized LiDAR map is constructed based on scene coding,which is represented with nodes,and each node contains a polarized LiDAR image,a point cloud scene coding,and a global pose.Secondly,a multi-scale localization of intelligent vehicles is readily realized based on the polarized LiDAR map,which contains coarse localization based on global positioning system(GPS)and motion model,node-level localization based on a histogram filter,and metric-level localization based on GICP algorithm.Finally,the proposed method is verified by the campus road dataset and the public KITTI dataset.The experimental results demonstrate that the accuracies of the node-level localization are 99.6％and 96.9％,the average localization errors are 0.34 m and 0.21 m,and the proposed method has strong robustness to different types of LiDAR sensors and different environments.
Keywords：intelligent vehicleLiDAR localizationmap matchingSiamese network
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 521-530 )