A probabilistic approach to Hough localization

Autonomous navigation for mobile robots performing complex tasks over long periods of time requires effective and robust self-localization techniques. We describe a probabilistic approach to self-localization that integrates Kalman filtering with map matching based on the Hough transform. Several systematic experiments for evaluating the approach have been performed both on a simulator and on soccer robots embedded in the RoboCup environment.