Road geometry estimation for urban semantic maps using open data

Complex robotic tasks require the use of knowledge that cannot be acquired with the sensor repertoire of a mobile, autonomous robot alone. For robots navigating in urban environments, geospatial open data repositories such as OpenStreetMap provide a source for such knowledge. We propose the integration of a 3D metric environment representation with the semantic knowledge from such a data base. The application we describe uses street network information from OpenStreetMap to improve street geometry information determined from laser data. This approach is evaluated on a challenging data set of the Munich inner city.