Particle-based dynamic occupancy maps were proposed in recent years to model the obstacles in dynamic environments. Current particle-based maps describe the occupancy status in discrete grid form and suffer from the grid size problem, wherein a large grid size is unfavorable for motion planning, while a small grid size lowers efficiency and causes gaps and inconsistencies. To tackle this problem, this paper generalizes the particle-based map into continuous space and builds an efficient 3D egocentric local map. A dual-structure subspace division paradigm, composed of a voxel subspace division and a novel pyramid-like subspace division, is proposed to propagate particles and update the map efficiently with the consideration of occlusions. The occupancy status of an arbitrary point in the map space can then be estimated with the particles' weights. To further enhance the performance of simultaneously modeling static and dynamic obstacles and minimize noise, an initial velocity estimation approach and a mixture model are utilized. Experimental results show that our map can effectively and efficiently model both dynamic obstacles and static obstacles. Compared to the state-of-the-art grid-form particle-based map, our map enables continuous occupancy estimation and substantially improves the performance in different resolutions.

翻译：近年来，基于粒子的动态占据地图被提出用于对动态环境中的障碍物进行建模。现有粒子地图以离散网格形式描述占据状态，存在网格尺寸问题：大网格不利于运动规划，而小网格降低效率并导致间隙与不一致性。针对此问题，本文将粒子地图推广至连续空间，构建了高效的三维自我中心局部地图。提出一种由体素子空间划分与新型金字塔状子空间划分组成的双结构子空间划分范式，可在考虑遮挡的情况下高效传播粒子并更新地图。通过粒子权重，可估计地图空间中任意点的占据状态。为进一步提升对静态与动态障碍物同步建模的性能并抑制噪声，引入了初始速度估计方法与混合模型。实验结果表明，所提出地图能够有效且高效地对动态和静态障碍物进行建模。与现有最先进的网格形式粒子地图相比，本文地图实现了连续占据估计，并在不同分辨率下显著提升了性能。