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.
@article{arxiv.2110.01749,
title = {Set-theoretic Localization for Mobile Robots with Infrastructure-based Sensing},
author = {Xiao Li and Yutong Li and Nan Li and Anouck Girard and Ilya Kolmanovsky},
journal= {arXiv preprint arXiv:2110.01749},
year = {2022}
}