Map building for a mobile robot from sensory data

A method for building a three-dimensional (3-D) world model for a mobile robot from sensory data derived from outdoor scenes is presented. The 3-D world model consists of four kinds of maps: a physical sensor map, a virtual sensor map, a local map, and a global map. First, a range image (physical sensor map) is transformed to a height map (virtual sensor map) relative to the mobile robot. Next, the height map is segmented into unexplored, occluded, traversable and obstacle regions from the height information. Moreover, obstacle regions are classified into artificial objects or natural objects according to their geometrical properties such as slope and curvature. A drawback of the height map (recovery of planes vertical to the ground plane) is overcome by using multiple-height maps that include the maximum and minimum height for each point on the ground plane. Multiple-height maps are useful not only for finding vertical planes but also for mapping obstacle regions into video images for segmentation. Finally, the height maps are integrated into a local map by matching geometrical parameters and by updating region labels. The results obtained using landscape models and the autonomous land vehicle simulator of the University of Maryland are shown, and constructing a global map with local maps is discussed