The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach

TL;DR

This paper introduces a hierarchical Bayesian method for the revisiting problem in mobile robot map building, improving decision accuracy in recognizing previously explored areas through environment modeling and multi-robot data integration.

Contribution

It applies hierarchical Bayesian estimation with a hidden Markov model and Dirichlet priors to enhance map merging and revisiting decisions in robot navigation.

Findings

Significant improvement over existing methods in revisiting accuracy

Effective environment modeling with hierarchical Bayesian approach

Successful application to multi-robot map merging scenarios

Abstract

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.