Monte Carlo Localization for Mobile Robots

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dc.contributor.author Burgard, Wolfram
dc.contributor.author Dellaert, Frank
dc.contributor.author Fox, Dieter
dc.contributor.author Thrun, Sebastian
dc.date.accessioned 2008-05-13T13:18:32Z
dc.date.available 2008-05-13T13:18:32Z
dc.date.issued 1999
dc.identifier.uri http://hdl.handle.net/1853/21570
dc.description.abstract To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem. However, current methods still face considerable hurdles. In particular, the problems encountered are closely related to the type of representation used to represent probability densities over the robot’s state space. Recent work on Bayesian filtering with particle-based density representations opens up a new approach for mobile robot localization, based on these principles. In this paper we introduce the Monte Carlo Localization method, where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. By using a sampling-based representation we obtain a localization method that can represent arbitrary distributions. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location. It is faster, more accurate and less memory-intensive than earlier grid-based methods. en_US
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Densities en_US
dc.subject Global localization en_US
dc.subject Position estimation and tracking en_US
dc.title Monte Carlo Localization for Mobile Robots en_US
dc.type Paper en_US
dc.contributor.corporatename Carnegie-Mellon University. Computer Science Dept.
dc.contributor.corporatename Universität Bonn. Institut für Informatik III


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