Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap

TL;DR

This paper introduces a formal framework that integrates odometric data and geometrical constraints into Hidden Markov Models for robot navigation, improving learning efficiency and robustness.

Contribution

It presents a novel method for incorporating geometrical information into HMMs and POMDPs, enhancing their applicability in robot navigation tasks.

Findings

Improved learning efficiency with fewer iterations.

Enhanced robustness to data reduction.

Validated effectiveness on real and simulated robot data.

Abstract

Hidden Markov models (HMMs) and partially observable Markov decision processes (POMDPs) provide useful tools for modeling dynamical systems. They are particularly useful for representing the topology of environments such as road networks and office buildings, which are typical for robot navigation and planning. The work presented here describes a formal framework for incorporating readily available odometric information and geometrical constraints into both the models and the algorithm that learns them. By taking advantage of such information, learning HMMs/POMDPs can be made to generate better solutions and require fewer iterations, while being robust in the face of data reduction. Experimental results, obtained from both simulated and real robot data, demonstrate the effectiveness of the approach.