センサ情報の統合と理解による移動ロボットのための世界モデルの構築

抄録

This paper presents a method for building a 3-D world model for a mobile robot from sensory data derived from outdoor scenes. 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) with respect to the mobile robot. 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 which include the maximum and minimum heights 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 the video image for segmentation. Finally, height maps are integrated into a local map by matching geometical parameters and by updating region labels. We show the results obtained using landscape models and ALV simulator of the University of Maryland.