Hierarchical path planning on probabilistically labeled polygons

We consider the case of a robot that must find ways in an initially unknown and often complex cross-country environment. Our approach relies on a particular model of the environment built from 3D data: the environment is represented by a hierarchical polygonal structure, in which the probabilities of terrain classes are updated as the robot moves. We describe how to define traversability costs within such a structure, taking into account the label probabilities, and explain how a shortest path search can produce a sequence of polygonal cells that must be crossed.