碰撞容许点树：适用于SIMD的最近邻方法用于快速碰撞检测
Collision-Affording Point Trees: SIMD-Amenable Nearest Neighbors for Fast Collision Checking
AI总结：
本文提出碰撞容许点树（CAPT）数据结构，加速机器人与点云的碰撞检测查询一个数量级，使基于采样的规划器能在毫秒级生成路径，并辅以空间填充曲线过滤算法，实现实时规划。
摘要：
针对传感器数据的运动规划常常是实时机器人控制中的关键瓶颈。对于基于采样的运动规划器（其对于如机械臂等的高维系统非常有效），最耗时的组件是碰撞检测。我们提出了一种新颖的空间数据结构，即碰撞容许点树（CAPT）：一种点云的精确表示，可将机器人与点云之间的碰撞检测查询加速一个数量级，在包含数千个点的3D场景中平均查询时间小于10纳秒。借助CAPT，基于采样的规划器可以在不到一毫秒的时间内生成有效且高质量的路径，在消费级CPU的单线程上，端到端总计算时间快于60帧每秒。我们还提出了一种基于空间填充曲线的点云过滤算法，该算法在保持结构的同时减少点云中的点数。我们的方法使机器人能够在感知环境中以实时速度进行规划，为高维系统在动态、变化和未建模环境中的规划应用开辟了潜力。
英文摘要：
Motion planning against sensor data is often a critical bottleneck in real-time robot control. For sampling-based motion planners, which are effective for high-dimensional systems such as manipulators, the most time-intensive component is collision checking. We present a novel spatial data structure, the collision-affording point tree (CAPT): an exact representation of point clouds that accelerates collision-checking queries between robots and point clouds by an order of magnitude, with an average query time of less than 10 nanoseconds on 3D scenes comprising thousands of points. With the CAPT, sampling-based planners can generate valid, high-quality paths in under a millisecond, with total end-to-end computation time faster than 60 FPS, on a single thread of a consumer-grade CPU. We also present a point cloud filtering algorithm, based on space-filling curves, which reduces the number of points in a point cloud while preserving structure. Our approach enables robots to plan at real-time speeds in sensed environments, opening up potential uses of planning for high-dimensional systems in dynamic, changing, and unmodeled environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
