Toward Generating Labeled Maps from Color and Range Data for Robot Navigation

This paper addresses the problem of extracting information from range and color data acquired by a mobile robot in urban environments. Our approach extracts geometric structures from clouds of 3-D points and regions from the corresponding color images, labels them based on prior models of the objects expected in the environment - buildings in the current experiments - and combines the two sources of information into a composite labeled map. Ultimately, our goal is to generate maps that are segmented into objects of interest, each of which is labeled by its type, e.g., buildings, vegetation, etc. Such a map provides a higher-level representation of the environment than the geometric maps normally used for mobile robot navigation. The techniques presented here are a step toward the automatic construction of such labeled maps.