Building a 3D world model for mobile robot from sensory data

The author presents a method for building a 3D world model for a mobile robot from sensory data. The model consists of three kinds of maps: a sensor map, a local map, and a global map. A range image (sensor map) is transformed to a height map (local map) with respect to a mobile robot. The height map is segmented into four categories (unexplored, occluded, traversable, and obstacle regions) for obstacle detection and path planning. Obstacle regions are classified into artificial objects or natural objects using both the height image and video image. One drawback of height map-the recovery of vertical planes-is overcome by the utilization of multiple height maps which include the maximum and minimum heights of each point, and the number of points in the range image mapped into one point in the height map. The multiple height map is useful not only for finding vertical planes in the height map but also for segmentation of the video image. Height maps are integrated into a global map by matching geometrical properties and updating region labels