A Hierarchical Bayesian Approach to the Revisiting Problem in Mobile Robot Map Building
Submitted by mkrainin on Tue, 2012-02-28 12:21
| Title | A Hierarchical Bayesian Approach to the Revisiting Problem in Mobile Robot Map Building |
| Publication Type | Book Chapter |
| Year of Publication | 2005 |
| Authors | Fox D, Ko J, Konolige K, Stewart B |
| Editor | Dario P, Chatila R |
| Book Title | Robotics Research: The Eleventh International Symposium |
| Series Title | Springer Tracts in Advanced Robotics (STAR) |
| Publisher | Springer 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> |
| Downloads | |
| Citation Key | Fox04Hie |
Last changed Wed, 2012-03-07 15:08

cs.