2.5-Dimensional Grid Mapping from Stereo Vision for Robotic Navigation

In this paper we propose a new method for environment mapping with three-dimensional information from visual information for robotic accurate navigation. Many approaches of 3D mapping using occupancy grid typically requires high computational effort to both build and store the map. We introduce an occupancy-elevation grid mapping, which is a discrete mapping approach, where each cell stores the occupancy probability, the height of the terrain at current place in the environment and the variance of this height value. This 2.5-dimensional representation allows that a mobile robot to know whether a place in the environment is occupied by an obstacle and the height of this obstacle, thus, it can decide if is possible to traverse the obstacle. Sensorial information necessary to construct the map is provided by a stereo vision system, which has been modelled with a robust probabilistic approach, considering the noise present in the stereo processing. The resulting maps favours the execution of tasks like decision making in the autonomous navigation, exploration, localization and path planning. Experiments carried out with a real mobile robots demonstrates that this proposed approach yields useful maps for robot autonomous navigation.