Global urban localization based on road maps

This paper presents a method to perform localization in urban environments using segment-based maps together with particle filters. In the proposed approach, the likelihood function is generated as a grid, derived from segment-based maps. The scheme can efficiently assign weights to the particles in real time, with minimum memory requirements and without any additional pre-filtering procedure. Multi-hypotheses cases are handled transparently by the filter. A local history-based observation model is formulated as an extension to deal with 'out-of-map' navigation cases. This feature is highly desirable since the map can be incomplete, or the vehicle can be actually located outside the boundaries of the provided map. The system behaves like a 'virtual GPS', providing global localization in urban environments, without using an actual GPS. Experimental results show the performance of the proposed architecture in large scale urban environments using route network description (RNDF) segment-based maps.