Probabilistic traversability map building for autonomous navigation

For the successful navigation of an unmanned vehicle, it is important to determine the traversable area reliably. Traditional occupancy grid mapping calculates the presence of obstacle probabilistically, however cannot provide meaningful information on traversability of the given area. In this paper, we address problem of building a traversability map for unmanned ground vehicle. Map building consists of two main parts; first part is the fusion of terrain recognition map and terrain model map. Second part is sequential update of the traversablity map. Maps are built from each sensors separately and fusion takes place to combine the maps. The resulting map is added to the existing map for update. The algorithm is implemented to an unmanned ground vehicle for field-test. Experiments are conducted under different conditions to evaluate the robustness of the algorithm.