用分布式的微型测距传感器，精准估算机器人周围物体的运动流。
Estimating Scene Flow in Robot Surroundings with Distributed Miniaturized Time-of-Flight Sensors
- 通过聚类与ICP算法，从稀疏噪声点云中推导密集运动流。
- 在24个传感器实验中，运动方向和速度误差符合传感器噪声水平。
- 适合需要实时环境感知的移动机器人，尤其在低密度数据下表现稳健。
机器人周围人体或物体的运动追踪对提升安全动作与反应至关重要。本文提出一种基于分布式微型时间飞行（ToF）传感器的场景流估计方法，这些传感器安装在机器人本体上，采集低密度且含噪的点云数据。该方法通过连续帧点云聚类，并应用迭代最近点（ICP）算法，生成稠密运动流；同时引入适应性策略以降低传感器噪声与点云稀疏性的影响。具体包括基于适应度的点分类，区分静止与运动点，以及通过内点剔除优化几何对应关系。实验使用24个ToF传感器，在不同控制速度下对物体运动进行速度估计。结果表明，该方法能稳定估计运动方向与大小，误差水平与传感器噪声一致。
原文摘要 · Abstract (English)
Tracking motions of humans or objects in the surroundings of the robot is essential to improve safe robot motions and reactions. In this work, we present an approach for scene flow estimation from low-density and noisy point clouds acquired from miniaturized Time of Flight (ToF) sensors distributed on the robot body. The proposed method clusters points from consecutive frames and applies Iterative Closest Point (ICP) to estimate a dense motion flow, with additional steps introduced to mitigate the impact of sensor noise and low-density data points. Specifically, we employ a fitness-based classification to distinguish between stationary and moving points and an inlier removal strategy to refine geometric correspondences. The proposed approach is validated in an experimental setup where 24 ToF are used to estimate the velocity of an object moving at different controlled speeds. Experimental results show that the method consistently approximates the direction of the motion and its magnitude with an error which is in line with sensor noise.
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