Efficient coverage of 3D environments with humanoid robots using inverse reachability maps

Covering a known 3D environment with a robot's camera is a commonly required task, for example in inspection and surveillance, mapping, or object search applications. In addition to the problem of finding a complete and efficient set of view points for covering the whole environment, humanoid robots also need to observe balance, energy, and kinematic constraints for reaching the desired view poses. In this paper, we approach this high-dimensional planning problem by introducing a novel inverse reachability map representation that can be used for fast pose generation and combine it with a next-best-view algorithm. We implemented our approach in ROS and tested it with a Nao robot on both simulated and real-world scenes. The experiments show that our approach enables the humanoid to efficiently cover room-sized environments with its camera.