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.