Probabilistic robotic grid mapping based on occupancy and elevation information

This paper proposes an alternative environment mapping method for accurate robotic navigation based on 3D information. Typical techniques for 3D mapping using occupancy grid require intensive computational workloads to both build and store the map. We introduce an occupancy-elevation grid (OEG) mapping technique, which is a discrete mapping approach where each cell represents the occupancy probability, the height of the terrain and its variance. This representation allows a mobile robot to know if a spot in the environment is occupied by an obstacle and the height of such obstacle, thus, it can decide if it is possible to traverse the obstacle. In general, the map representation introduced can be used in conjunction with any kind of distance sensor. In this work, we have used laser range data with a probabilistic treatment. The resulting maps allow the execution of tasks like decision making for autonomous navigation, exploration, localization and path planning, considering the existence and the height of the obstacles. Experiments carried out with real data demonstrate that the proposed approach yields useful maps for autonomous navigation.