Local Navigation in Rough Terrain using Omnidirectional Height

Terrain perception is essential for navigation planning in rough terrain. In this paper, we propose to generate robot-centered 2D drivability maps from eight RGB-D sensors measuring the 3D geometry of the terrain 360 ◦ around the robot. From a 2.5D egocentric height map, we assess drivability based on local height differences on multiple scales. The maps are then used for local navigation planning and precise trajectory rollouts. We evaluated our approach during the DLR SpaceBot Cup competition, where our robot successfully navigated through a challenging arena, and in systematic lab experiments.