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.

翻译：本文提出了一种紧凑、累积且可合并的概率体素建图方法，旨在提升激光雷达里程计的性能、精度与内存效率。概率体素建图通常需要存储历史点云数据并在每次迭代中重新遍历这些数据以更新不确定性，这消耗了大量内存空间与CPU计算周期。为解决此问题，我们提出了一种双重策略。首先，我们为概率体素引入了一种紧凑的无点表示方法，并推导出无需缓存原始点云的平面不确定性累积更新公式。我们的体素结构仅跟踪其内部点的预定统计量集合。该方法将时间复杂度从$O(MN)$降低至$O(N)$，空间复杂度从$O(N)$降低至$O(1)$，其中$M$为迭代次数，$N$为点数。其次，为进一步最小化内存占用并提升建图精度，我们提出一种策略，利用现实世界中的几何特征，动态合并与同一物理平面对应的体素。我们的合并策略并非在每次迭代中持续扫描这些可合并体素，而是将体素累积于局部敏感哈希中，并按需延迟触发合并。这种按需合并机制不仅以最小的计算开销减少了内存占用，还通过跨体素去噪提高了定位精度。实验表明，与现有最优方法相比，本方法在精度上提升20%，速度提升20%，内存消耗降低70%。