Estimating Scene Flow in Robot Surroundings with Distributed Miniaturized Time-of-Flight Sensors
This work addresses motion tracking for robot safety, but it is incremental as it builds on existing methods like ICP with adaptations for specific sensor limitations.
The paper tackles the problem of estimating scene flow from low-density, noisy point clouds using distributed miniaturized Time-of-Flight sensors on a robot, achieving motion direction and magnitude estimation with errors consistent with sensor noise in controlled experiments.
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.