The revisiting problem in mobile robot map building: a hierarchical bayesian approach

Authors:
Benjamin Stewart;Jonathan Ko;Dieter Fox;Kurt konolige
Affiliations:
Dept. of Computer Science & Engineering, University of Washington, Seattle, WA;Dept. of Computer Science & Engineering, University of Washington, Seattle, WA;Dept. of Computer Science & Engineering, University of Washington, Seattle, WA;Artificial Intelligence Center, SRI International, Menlo Park, CA
Venue:
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Year:
2002

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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 previously-built 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 by 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 altemative methods.