Stereoscopic vision is an appropiate 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 deal with errors in region matching is proposed, and the results in the presence of noise are analyzed. The selection of an appropiate 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.