Intelligent Vehicle Localization Based on Polarized LiDAR Representation and Siamese Network

Intelligent vehicle localization based on 3D Light Detection And Ranging (LiDAR) is still a challenging task in map storage and the efficiency and accuracy of map matching. A lightweight node-level polarized LiDAR map is constructed by a series of nodes with a 2D polarized LiDAR image, a polarized LiDAR fingerprint, and sensor pose, while the polarized LiDAR image encodes a 3D cloud using a multi-channel image format, and the fingerprint is extracted and trained using Siamese network. An intelligent vehicle localization method is also proposed by matching with the polarized LiDAR map. Firstly, Siamese network is used to model the similarity of the query and map fingerprints for fast and coarse map matching. Then a Second-Order Hidden Markov Model (HMM2)-based map sequence matching method is used to find the nearest map node. Finally, the vehicle is readily localized using 3D registration. The proposed method is tested using the actual field data and the public KITTI database. The results indicate that the proposed method can achieve map matching accuracy up to 96% and 30cm localization accuracy with robustness in different types of LiDAR sensors and different environments.