Self-localization is an essential technology for autonomous vehicles. This work proposed a simple and effective LiDAR odometry based on the segmentation voxel map termed SVM-LO. Unlike other LiDAR odometry, the SVM-LO is unlimited by LiDAR scanning modes and environment changes. Our approach consists of two components: the front-end, which acquires key points and the local map, and the back-end, which obtains the robust poses by adaptive mix iterative closest point (AM-ICP). We design an adaptive voxel classifier for extracting more stable key points and constructing the segmentation voxel map. To guarantee system real-time performance, we propose the adaptive dual-subsampling module and optimized the store strategy of the local map. We conduct extensive experiments comparing SVM-LO with state-of-the-art technology on different datasets, the results demonstrate that the SVM-LO is generic, robust, and accurate. Our system is able to run in real-time under multi-scale scenarios, narrow scenarios, and degradation scenarios. SVM-LO eschews reliance on LiDAR scanning characteristics and other sensors, thus it can be widely used in a variety of autonomous driving scenarios.
SVM-LO: An Accurate, Robust, Real-time LiDAR Odometry with Segmentation Voxel Map for Autonomous Vehicles
2024-09-24
3159955 byte
Conference paper
Electronic Resource
English
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