Autonomous navigation for mobile robots performing complex tasks over long periods of time requires effec-tive and robust self-localization techniques. In this paper we describe a probabilistic approach to self-localization that integrates Kalman filtering with map matching based on the Hough Transform. Several sys-tematic experiments for evaluating the approach have been performed both on a simulator and on soccer robots embedded in the RoboCup environment. 1
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Iocchi et al. (2002) studied this question.
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