  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.
