一句话：通过缓存访问优化、体素重排序与工作流并行化加速 OctoMap 的 3D 占据建图，最高取得 3.02× 加速。
3D 建图系统广泛应用于自主机器人导航、3D 可视化与 AR/VR，其中 OctoMap 是采用八叉树结构兼顾空间效率的主流框架。然而，OctoMap 因代价高昂的内存访问导致更新缓慢，成为性能瓶颈。本文提出 OctoCache，一个从软件层面加速 OctoMap 的系统，包含三项优化：(1) 优化的缓存内存访问，(2) 改进的体素排序，(3) 工作流并行化。作者在 3D 环境构建任务上评估，相比标准 OctoMap 取得 45.63%~88.01% 的加速；在无人机导航场景中部署后，获得最高 3.02× 的加速，并使任务完成时间最多缩短 28%。结果表明 OctoCache 能有效提升自主导航中的 3D 建图效率。
3D mapping systems are crucial for creating digital representations of physical environments, widely used in autonomous robot navigation, 3D visualization, and AR/VR. This paper focuses on OctoMap, a leading 3D mapping framework using an octree-based structure for spatial efficiency. However, OctoMap's performance is limited by slow updates due to costly memory accesses. We introduce OctoCache, a software system that accelerates OctoMap through (1) optimized cache memory access, (2) refined voxel ordering, and (3) workflow parallelization. OctoCache achieves speedups of 45.63%~88.01% in 3D environment construction tasks compared to standard OctoMap. Deployed in UAV navigation scenarios, OctoCache demonstrates up to 3.02× speedup and reduces mission completion time by up to 28%. These results highlight OctoCache's potential to enhance 3D mapping efficiency in autonomous navigation, advancing robotics and environmental modeling.
@inproceedings{ChenLWHYRL25,
title = {OctoCache: Caching Voxels for Accelerating 3D Occupancy Mapping in Autonomous Systems},
author = {Peiqing Chen and Minghao Li and Zishen Wan and Yu-Shun Hsiao and Minlan Yu and Vijay Janapa Reddi and Zaoxing Liu},
booktitle = {ASPLOS},
year = {2025},
doi = {10.1145/3676641.3716263}
}