Today I read a paper titled “The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach”
The abstract is:
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.