B. Stewart, J. Ko, D. Fox, and K. Konolige.

The revisiting problem in mobile robot map building: A hierarchical Bayesian approach.

Proc. of the Conference on Uncertainty in Artificial Intelligence (UAI-03)


 

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.

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Full paper [pdf] (724 kb, 8 pages).
More background information on hyperparameters is provided in our ISRR-03 paper.


 


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