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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Fox, D., Ko, J., Konolige, K., & Stewart, B. (2005). A hierarchical Bayesian approach to the revisiting problem in mobile robot map building. Springer Tracts in Advanced Robotics, 15, 60–69. https://doi.org/10.1007/11008941_7
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