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

A Hierarchical Bayesian Approach to the Revisiting Problem in Mobile Robot Map Building.

Proc. of the International Symposium of Robotics Research (ISRR-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 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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Full paper [pdf] (444 kb, 10 pages)
This paper is closely related to the two papers appearing at IROS-03 and UAI-03. It provides more background on Dirichlet priors than the UAI paper.
 


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