Set-theoretic Localization for Mobile Robots with Infrastructure-based Sensing

TL;DR

This paper presents a set-theoretic localization method for mobile robots that robustly estimates position and orientation using infrastructure-based sensing, handling uncertainties effectively in simulation and real-world scenarios.

Contribution

It introduces a novel set-theoretic approach for robot localization that over-bounds the robot's state considering sensor and motion uncertainties, with theoretical and practical validation.

Findings

Robust localization under sensor noise and initialization uncertainties

Successful application to automated parking and real-world robot experiments

Outperforms FastSLAM in robustness to uncertainties

Abstract

In this paper, we introduce a set-theoretic approach for mobile robot localization with infrastructure-based sensing. The proposed method computes sets that over-bound the robot body and orientation under an assumption of known noise bounds on the sensor and robot motion model. We establish theoretical properties and computational approaches for this set-theoretic localization approach and illustrate its application to an automated valet parking example in simulations and to omnidirectional robot localization problems in real-world experiments. We demonstrate that the set-theoretic localization method can perform robustly against uncertainty set initialization and sensor noises compared to the FastSLAM.