MAP BUILDING FOR A MOBILE ROBOT FROM SENSORY DATA

The development of an autonomous land vehicle (ALV) is a central problem in artificial intelligence and robotics, and has been extensively studied. To perform visual navigation, a robot must gather information about its environment through external sensors, interpret the output of these sensors, construct a scene map and a plan sufficient for the task at hand, and then monitor and execute the plan. As a first step, real time visual navigation systems for road following were developed in which simple methods for detecting road edges were applied in simple environments. For even slightly more complicated scenes, the difficulty of the problem increases dramatically, therefore a world model such as a map could be very important for successful navigation through such environments. 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 image for segmentation. Finally, height maps are integrated into a local map by matching geometrical parameters and by updating region labels. The results obtained using landscape models and ALV simulator of the University of Maryland are shown, and constructing a global map with local maps is discussed. © 1990 IEEE