Confidence-rich Localization and Mapping based on Particle Filter for Robotic Exploration

TL;DR

This paper introduces a Rao-Blackwellized particle filter-based approach for confidence-rich localization and mapping, enhancing robot exploration by accurately estimating pose uncertainty and improving exploration efficiency.

Contribution

It extends confidence-rich mutual information with measurable pose uncertainty and develops a new weighting method to improve localization accuracy without scan matching.

Findings

Enhanced localization accuracy demonstrated in simulations and experiments

Improved exploration performance in unknown environments

Effective integration of pose uncertainty into information metrics

Abstract

This paper mainly studies the localization and mapping of range sensing robots in the confidence-rich map (CRM) and then extends it to provide a full state estimate for information-theoretic exploration. Most previous works about active simultaneous localization and mapping and exploration always assumed the known robot poses or utilized inaccurate information metrics to approximate pose uncertainty, resulting in imbalanced exploration performance and efficiency in the unknown environment. This inspires us to extend the confidence-rich mutual information (CRMI) with measurable pose uncertainty. Specifically, we propose a Rao-Blackwellized particle filter-based localization and mapping scheme (RBPF-CLAM) for CRM, then we develop a new closed-form weighting method to improve the localization accuracy without scan matching. We further derive the uncertain CRMI (UCRMI) with the weighted particles by a more accurate approximation. Simulations and experimental evaluations show the localization accuracy and exploration performance of the proposed methods.