GIS-based topological robot localization through LIDAR crossroad detection

While navigating in areas with weak or erroneous GPS signals such as forests or urban canyons, correct map localization is impeded by means of contradicting position hypotheses. Thus, instead of just utilizing GPS positions improved by the robot's ego-motion, this paper's approach tries to incorporate crossroad measurements given by the robots perception system and topological informations associated to crossroads within a pre-defined road network into the localization process. We thus propose a new algorithm for crossroad detection in LIDAR data, that examines the free space between obstacles in an occupancy grid in combination with a Kalman filter for data association and tracking. Hence rather than correcting a robot's position by just incorporating the robot's ego-motion in the absence of GPS signals, our method aims at data association and correspondence finding by means of detected real world structures and their counterparts in predefined, maybe even handcrafted, digital maps.