  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
