Robust Region-Based Stereo Vision to Build Environment Maps for Robotics Applications Ángeles López, Filiberto Pla

Abstract Stereoscopic vision is an appropriate tool for building maps of the environment of a robot. When matching regions of the images, segmentation errors should be avoided. In this paper, an algorithm to dela with errors in region matching is proposed, and the results in the presence of noise are analyzed. The selection of an appropriate similarity criterion to create the initial nodes in the graph-based matching process is very important for reducing the time of computation considerably. The experimental results show that the method is robust in the presence of noise.