  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.

Artificial Intelligence;Robotics