Global Localization on OpenStreetMap Using 4-bit Semantic Descriptors
Fan Yan, Olga Vysotska, and Cyrill Stachniss
University of Bonn, Germany
Localization is an essential capability of mobile vehicles such as robots or autonomous cars. Localization systems that do not rely on GNSS typically require a map of the environment to compare the local sensor readings to the map. In most cases, building such a model requires an explicit mapping phase for recording sensor data in the environment. In this paper, we investigate the problem of localizing a mobile vehicle equipped with a 3D LiDAR scanner, driving on urban roads without mapping the environment beforehand. We propose an approach that builds upon publicly available map information from OpenStreetMap and turns them into a compact map representation that can be used for Monte Carlo localization. This map requires to store only a tiny 4-bit descriptor per location and is still able to globally localize and track a vehicle. We implemented our approach and thoroughly tested it on real-world data using the KITTI datasets. The experiments presented in this paper suggest that we can estimate the vehicle pose effectively only using OpenStreetMap data.