An Integrative Framework for Global Self-Localization
2001 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2001)
Baden-Baden, Germany, August 20-22, 2001
Joachim Weber, Lutz Franken, Klaus-Werner Jörg, Klaus Schmitt, Ewald von Puttkamer
Concerning the robustness of mobile robot navigation, global self-localization is a key feature for many service applications. In this paper we describe an effcient Bayesian approach for hybrid topological/metric navigation, which is designed to exploit information from multiple sources of sensor data. Experiments with a combination of odometry/laserscans/computer vision in a partly monotonous environment show the system's ability to generate initial position hypotheses, to cope with environmental ambiguities and to recover from severe position errors.
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