Robust Global Localization Using Clustered Particle Filtering

TL;DR

This paper introduces a clustered particle filtering extension to Monte Carlo Localization that improves robot localization accuracy in highly symmetrical environments where traditional methods struggle.

Contribution

The paper proposes a novel clustering approach within MCL to address sample impoverishment and improve localization in symmetric environments.

Findings

Successfully localizes in symmetric environments where standard MCL fails

Improves robustness of particle filtering in complex environments

Demonstrates better pose estimation accuracy

Abstract

Global mobile robot localization is the problem of determining a robot's pose in an environment, using sensor data, when the starting position is unknown. A family of probabilistic algorithms known as Monte Carlo Localization (MCL) is currently among the most popular methods for solving this problem. MCL algorithms represent a robot's belief by a set of weighted samples, which approximate the posterior probability of where the robot is located by using a Bayesian formulation of the localization problem. This article presents an extension to the MCL algorithm, which addresses its problems when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot. The problem arises from the fact that sample sets in MCL often become impoverished, when samples are generated according to their posterior likelihood. Our approach incorporates the idea of clusters of samples and modifies the proposal distribution considering the probability mass of those clusters. Experimental results are presented that show that this new extension to the MCL algorithm successfully localizes in symmetric environments where ordinary MCL often fails.