Enhanced LiDAR-Based Localization via Multiresolution Iterative Closest Point Algorithms and Feature Extraction

Abstract

Vehicle localization is a critical component in autonomous driving systems, and light detection and ranging (LiDAR)-based methods have become increasingly popular for this task. In this article, we present a novel vehicle localization approach based on the point cloud map generated from LiDAR data. In particular, our approach first uses semantic segmentation and feature point extraction techniques to create an efficient feature point map and a long-lasting map from LiDAR sequences with corresponding poses. We then introduce a map-based online localization method that achieves precise vehicle localization using both LiDAR scans and the two point cloud maps, along with a multiresolution ICP strategy. Comprehensive evaluations are conducted on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) odometry dataset and the collected results demonstrate superior performance over the existing literature in both odometry metrics and absolute translation error. Our multiresolution iterative closest point (ICP)-based method holds significant potential for map-based vehicle localization, offering promising prospects for application in autonomous driving and associated domains.