Localization on OpenStreetMap data using a 3D laser scanner

To determine the pose of a vehicle is a fundamental problem in mobile robotics. Most approaches relate the current sensor observations to a map generated with previously acquired data of the same system or by another system with a similar sensor setup. Unfortunately, previously acquired data is not always available. In outdoor settings, GPS is a very useful tool to determine a global estimate of the vehicles pose. Unfortunately, GPS tends to be unreliable in situations in which a clear view to the sky is restricted. Yet, one can make use of publicly available map material as prior information. In this paper, we describe an approach to localize a robot equipped with a 3D range scanner with respect to a road network created from OpenStreetMap data. To successfully localize a mobile robot we propose a road classification scheme for 3D range data together with a novel sensor model, which relates the classification results to a road network. Compared to other approaches, our system does not require the robot to actually travel on the road network. We evaluate our approach in extensive experiments on simulated and real data and compare favorably to two state-of-the-art methods on those data.