Detecting Usable Planar Regions for Legged Robot Locomotion

Awareness of the environment is essential for mobile robots. Perception for legged robots requires high levels of reliability and accuracy in order to walk stably in the types of complex, cluttered environments we are interested in. In this paper, we present a usable environmental perception algorithm designed to detect steppable areas and obstacles for the autonomous generation of desired footholds for legged robots. To produce an efficient representation of the environment, the proposed perception algorithm is desired to cluster point cloud data to planar regions composed of convex polygons. We describe in this paper the end-to-end pipeline from data collection to generation of the regions, where we first compose an octree in order to create a more efficient data representation. We then group the leaves in the tree using a nearest neighbor search into a planar region, which is composed of the concave hull of points that is decomposed into convex polygons. We present a variety of environments, and illustrate the usability of this approach by the Atlas humanoid robots walking over rough terrain. We also discuss various challenges we faced and insights we gained in the development of this approach.