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

Xiao Li, Yutong Li, Anouck Girard, Ilya Kolmanovsky

In this article, we propose a set-membership based localization approach for mobile robots using infrastructure-based sensing. Under an assumption of known uncertainties bounds of the noise in the sensor measurement and robot motion models, the proposed method computes uncertainty sets that over-bound the robot 2D body and orientation via set-valued motion propagation and subsequent measurement update from infrastructure-based sensing. We establish theoretical properties and computational approaches for this set-theoretic localization method and illustrate its application to an automated valet parking example in simulations, and to omnidirectional robot localization problems in real-world experiments. With deteriorating uncertainties in system parameters and initialization parameters, we conduct sensitivity analysis and demonstrate that the proposed method, in comparison to the FastSLAM, has a milder performance degradation, thus is more robust against the changes in the parameters. Meanwhile, the proposed method can provide estimates with smaller standard deviation values. [Github, Paper]