Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry
This work addresses the need for robust and fast LiDAR odometry in robotics and autonomous vehicles, representing an incremental improvement over existing methods.
The paper tackles the problem of accurate and efficient LiDAR odometry by proposing an adaptive voxel mapping method that uses probabilistic representations and coarse-to-fine organization, achieving high accuracy and efficiency on the KITTI dataset and adaptability in unstructured environments.
This paper proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane (or edge) 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. Outdoor experiments on unstructured environments with non-repetitive scanning LiDARs further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns. Our codes and dataset are open-sourced on Github