ロボット周囲のシーンフローを分散型小型ToFセンサで推定
Estimating Scene Flow in Robot Surroundings with Distributed Miniaturized Time-of-Flight Sensors
ロボット本体に分散配置した小型ToFセンサの低密度・ノイズを含む点群から、ICPと外れ値除去により物体の動き（シーンフロー）を推定する手法を提案し、24個のセンサで物体速度を実験的に検証した。
著者: Jack Sander, Giammarco Caroleo, Alessandro Albini, Perla Maiolino
分類: cs.RO, cs.CV
原文アブストラクト
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.
