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

TitleA Hierarchical Bayesian Approach to the Revisiting Problem in Mobile Robot Map Building
Publication TypeBook Chapter
Year of Publication2005
AuthorsFox D, Ko J, Konolige K, Stewart B
EditorDario P, Chatila R
Book TitleRobotics Research: The Eleventh International Symposium
Series TitleSpringer Tracts in Advanced Robotics (STAR)
PublisherSpringer Verlag
Abstract<p>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.</p>
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Last changed Wed, 2012-03-07 15:08