Abstract
This paper describes a traversability map construction for autonomous mobile robots. To represent a terrain, a plane is estimated in each grid from point cloud data using RANSAC algorithm. Traversability is analyzed from height, slope and roughness of the plane. Point cloud data becomes sparse in proportion to distance from stereo camera. Thus, point cloud sampling areas are expanded using stereo vision error model. Finally, we have run our rover testbed "Micro6" in Ura-Sabaku desert of Izu-Oshima island, and experimental results were shown that a proposed method validities.
