Set-theoretic Localization for Mobile Robots with Infrastructure-based Sensing
This work addresses localization for mobile robots using infrastructure-based sensing, presenting an incremental improvement with theoretical and computational developments.
The paper tackles mobile robot localization by introducing a set-theoretic approach that computes over-bounding sets for robot position and orientation using known noise bounds, and demonstrates robust performance against uncertainty and sensor noise compared to FastSLAM in simulations and real-world experiments.
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.