Learning Maps for Indoor Mobile Robot Navigation
Learning Maps for Indoor Mobile Robot Navigation Sebastian Thrun Arno BÈucken April 1996 CMU-CS-96-121 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 The ®rst author is partly and the second author exclusively af®liated with the Computer Science Department III of the University of Bonn, Germany, where part of this research was carried out. This research is sponsored in part by the National Science Foundation under award IRI-9313367, and by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. The views and conclusionscontained in this documentare those of the author and should not be interpreted as necessarily representing of®cial policies or endorsements, either expressed or implied, of NSF, Wright Laboratory or the United States Government. Keywords: autonomous robots, exploration, mobile robots, neural networks, occupancy grids, path planning, planning, robot mapping, topological maps Abstract Autonomous robots must be able to learn and maintain models of their environ- ments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits ef®cient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more ef®ciently, yet accurate and consistent topological maps are considerably dif®cult to learn in large-scale envi- ronments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using arti®cial neural networks and Bayesian integration.
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