Building a 3-D World Model for a Mobile Robot from Sensory Data.
Abstract
This paper presents a method for building a 3-D world model for a mobile robot from sensory data. The 3-D world model consists for three kinds for 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. First, the height map is segmented into four categories (local map) with respect to a mobile robot. First, the height map is segmented into four categories (unexplored, occluded, traversable,,and obstacle regions) for obstacle detection and path planning. Next, obstacle regions are classified into artificial objects (buildings, cars, and road sings, etc.) or natural objects (trees, bushes, etc.) using both the height image and video image. One drawback of the height map--the recovery of vertical planes--is overcome by the utilization of multiple height maps which include the maximum and minimum height 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. Finally, the height maps are integrated into a global map by matching geometrical properties and updating region labels. The method is tested on a model including many objects, such as trees, buildings, cars, and so on. Keywords: Robotics; Autonomous land vehicles; World model; Sensor map; Height map; Global map; Path planning; Range data; Artificial intelligence.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1988
- Accession Number
- ADA190346
Entities
People
- Minoru Asada
Organizations
- University of Maryland