Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry

This letter proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane feature that enables the probabilistic representation of the environment and accurate registration of a new LiDAR scan. We further analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the proposed voxel map to an iterated extended Kalman filter and construct a maximum a posteriori probability problem for pose estimation. Experiments on the open KITTI dataset show the high accuracy and efficiency of our method compared to other state-of-the-art methods. Experiments on indoor and unstructured outdoor environments with solid-state LiDAR and non-repetitive scanning LiDAR further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns (see our attached video(1)). Our codes and dataset are open-sourced on Github(2)

Automated summary

This study proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The method utilizes voxel maps to probabilistically represent the environment and accurately register new LiDAR scans, achieving high accuracy and efficiency compared to other state-of-the-art methods.