Stereo Vision-Based Obstacle Detection for Partially Sighted People

Obstacle avoidance is a major requirement for any technological aid aimed at helping partially sighted (TAPS) people to navigate safely. In this paper, a stereo vision-based algorithm (Ground Plane Obstacle Detection) is extended to detect small obstacles for TAPS using RANSAC dynamic recalibration and Kalman Filtering. Obstacle detection and false alarm are investigated probabilistically. Furthermore, a technique is developed to find objects by matching their edges with some heuristic criteria. Experiments show that obstacle edges are extracted much better with our dynamic recalibration approach and that objects can be found successfully by the edge matching technique.