C³P-VoxelMap: Compact, Cumulative and Coalescible Probabilistic Voxel Mapping

This work presents a compact, cumulative, and coalescible probabilistic voxel mapping method to enhance performance, accuracy, and memory efficiency in LiDAR odometry. Probabilistic voxel mapping requires storing past point clouds and re-iterating them to update the uncertainty at every iteration, which consumes large memory space and CPU cycles. To solve this problem, we propose a two-fold strategy. First, we introduce a compact point-free representation for probabilistic voxels and derive a cumulative update of the planar uncertainty without caching original point clouds. Our voxel structure only keeps track of a predetermined set of statistics for points that lie inside it. This method reduces the runtime complexity from O(MN) to O(N) and the space complexity from O(N) to O(1) where M is the number of iterations and N is the number of points. Second, to further minimize memory usage and enhance mapping accuracy, we provide a strategy to dynamically merge voxels associated with the same physical planes by taking advantage of the geometric features in the real world. Rather than constantly scanning for these coalescible voxels at every iteration, our merging strategy accumulates voxels in a locality-sensitive hash and triggers merging lazily. On-demand merging reduces memory footprint with minimal computational overhead and improves localization accuracy thanks to cross-voxel denoising. Experiments exhibit 20% higher accuracy, 20% faster performance, and 70% lower memory consumption than the state-of-the-art.