Localization in urban environments by matching sensor data to map information

This paper presents an outdoor localization algorithm for assistive devices such as wheelchairs or walkers in urban environments. By fusing GPS, map information, and odometry with the help of a Monte Carlo particle filter, we provide adequate pose estimates for the implementation of device - specific navigation systems. We demonstrate the robustness and precision of the presented solution by experimental test runs in a municipal scenario, and compare the achieved results against a Kalman filter based localizer that integrates odometry and rate of turn data coming from a sophisticated inertial measurement unit. The core contribution of this work is given by the extension of commonly used map matching techniques in the sense that we not only evaluate the road network, but also different kinds of mapped entities representing obstacles for the vehicle.