Uncertainty in Artificial Intelligence
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The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach
Benjamin Stewart, Jonathan Ko, Dieter Fox, Kurt Konolige
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 alternative methods.
Keywords:
Pages: 551-558
PS Link:
PDF Link: /papers/03/p551-stewart.pdf
BibTex:
@INPROCEEDINGS{Stewart03,
AUTHOR = "Benjamin Stewart and Jonathan Ko and Dieter Fox and Kurt Konolige",
TITLE = "The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "551--558"
}


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