Optical flow based obstacle detection in static environment

Abstract

Since the common approaches for estimating the time-to-contact employ the first order derivatives of the optical flow,such methods have the drawback of being sensitive to error in the estimates of optical flow.Based on the assumption that the robot was moving on a locally planar ground,a novel method for estimating the time-to-contact of obstacle was presented.Scale invariant feature transform(SIFT) matching of features between the image sequences captured from monocular camera was firstly applied.Then,the homography for the planar ground was robustly estimated based on the random sample consensus(RANSAC) algorithm,and the optical flow was estimated.After that,the time-to-contact was recovered and the obstacle map was finally constructed.Since the computation of the first order derivatives of optical flow is completely avoided,this method is robust to image noise.Experimental results of indoor and outdoor scenes show that the method can effectively detect obstacles based on the recovered time-to-contact.