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