论文/arXiv 2608.05586·2 个月前

PathCover：基于点云的随机迭代空间划分（RISP）沿路径快速凸分解

针对点云障碍下走廊生成实时瓶颈，提出RISP与PathCover，并在LiDAR数据、四旋翼仿真和四足真机中验证。

AI 整理

• 本体 SDK 与控制
• 感知与导航
• 仿真与训练

原文链接：PathCover: A Fast Convex Decomposition along a Path via Randomized Iterative Space Partitioning (RISP) on Point Clouds（arXiv 2608.05586 · 发布日期：2026-08-06）

PathCover: A Fast Convex Decomposition along a Path via Randomized Iterative Space Partitioning (RISP) on Point Clouds

针对点云障碍下走廊生成实时瓶颈，提出RISP与PathCover，并在LiDAR数据、四旋翼仿真和四足真机中验证。

核心要点

• 机器人导航需快速构建与障碍点分离的凸区域，现有走廊生成难满足传感器频率实时性。
• RISP从有限点云构造凸多面体，PathCover沿参考路径串联为重叠走廊，并证明终止、相交与覆盖。
• 合成和真实LiDAR基准显示，走廊生成与轨迹优化相较先进方法有数量级加速。
• 闭环四旋翼仿真和实体四足穿越展示其可接入下游运动规划器，完整代码已开源。

验证范围与局限：验证范围为合成与真实LiDAR数据、闭环四旋翼仿真和实体四足穿越任务。

英文摘要 (Abstract)

Autonomous robot navigation requires rapid construction of obstacle-points-separated convex regions for trajectory planning. When obstacles are represented as point clouds from LiDAR or depth cameras, these regions must be constructed directly from finite obstacle samples while providing suitable constraints for downstream optimization. However, existing corridor-generation methods often struggle to meet real-time, sensor-rate computational requirements. To address this bottleneck, we introduce RISP, a randomized algorithm that constructs convex polytopes from finite point-cloud data, and PathCover, which chains these polytopes along a reference path to form an overlapping corridor. We prove finite termination, sequential intersection, and complete path coverage with respect to the supplied finite point set. Under a probabilistic elimination condition, the sampling-and-elimination stage of RISP has expected O(n) time and unconditional worst-case O(n^2) time. Extensive benchmarks on synthetic and real-world LiDAR datasets demonstrate an order-of-magnitude speedup over state-of-the-art methods in both corridor generation as well as trajectory optimization, while closed-loop quadrotor simulation and a physical quadruped traversal demonstrate integration with downstream motion planners. These results establish corridor separation from the supplied point-cloud representation and practical implementation feasibility. Source code of the entire pipeline is available at https://github.com/kunalnk123690/PathCover.

作者：Kunal Sanjay Narkhede, Abhijeet M. Kulkarni, Guoquan P. Huang, Ioannis Poulakakis

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2608.05586：PathCover: A Fast Convex Decomposition along a Path via Randomized Iterative Space Partitioning (RISP) on Point Clouds访问 2026-10-21