Abstract
We present an application of hierarchical Bayesian estimation to robot map building. The revisiting problem occurs when a robot has to decide whether it is seeing a previouslybuilt portion of a map, or is exploring new territory. This is a difficult decision problem, requiring the probability of being outside of the current known map. To estimate this probability, we model the structure of a ”typical” environment as a hidden Markov model that generates sequences of views observed by a robot navigating through the environment. A Dirichlet prior over structural models is learned from previously explored environments. Whenever a robot explores a new environment, the posterior over the model is estimated using Dirichlet hyperparameters. Our approach is implemented and tested in the context of multi-robot map merging, a particularly difficult instance of the revisiting problem. Experiments with robot data show that the technique yields strong improvements over alternative methods.
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© 2005 Springer-Verlag Berlin Heidelberg
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Fox, D., Ko, J., Konolige, K., Stewart, B. (2005). A Hierarchical Bayesian Approach to the Revisiting Problem in Mobile Robot Map Building. In: Dario, P., Chatila, R. (eds) Robotics Research. The Eleventh International Symposium. Springer Tracts in Advanced Robotics, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11008941_7
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DOI: https://doi.org/10.1007/11008941_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23214-8
Online ISBN: 978-3-540-31508-7
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