C$^3$P-VoxelMap: Compact, Cumulative and Coalescible Probabilistic Voxel Mapping

TL;DR

This paper introduces a novel probabilistic voxel mapping method that significantly reduces memory and computational costs while improving accuracy in LiDAR odometry through a compact representation and dynamic voxel merging.

Contribution

The paper proposes a compact, point-free probabilistic voxel representation and a lazy, locality-sensitive voxel merging strategy to enhance efficiency and accuracy in mapping.

Findings

20% higher accuracy compared to state-of-the-art

20% faster performance

70% lower memory consumption

Abstract

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 on them to update the uncertainty every iteration, which consumes large memory space and CPU cycles. To solve this problem, we propose a two-folded 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 scanning for these coalescible voxels constantly at every iteration, our merging strategy accumulates voxels in a locality-sensitive hash and triggers merging lazily. On-demand merging not only reduces memory footprint with minimal computational overhead but also 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.