Stereo sketch: stereo vision-based target reaching behavior acquisition with occlusion detection and avoidance

In this paper, we proposed a method by which a stereo vision-based mobile robot learns to reach a target by detecting and avoiding occlusions. We call the internal representation that describes the learning behavior “stereo sketch”. First, an input scene is segmented into homogeneous regions by the enhanced ISODATA algorithm with minimum description length principle in terms of image coordinates and disparity information obtained from the fast stereo matching unit based on the coarse-to-fine control method. Then, in terms of the segmented regions including the target area and their occlusion status identified during the stereo and motion disparity estimation process, we construct a state space for the reinforcement learning method to obtain a target reaching behavior. As a result the robot can avoid obstacles without explicitly describing them. We give the computer simulation results and real robot implementation to show the validity of our method