Abstract
Gaussian Processes have been previously used to model wireless signals strengths and create location-signal strength mappings. Such mappings can be used for robot localization by computing the posterior probability distributions of robot's positions given signal strength measurements. This work proposes the use of a dual Monte Carlo Localization algorithm that uses such posterior distributions as perceptual likelihood. Our approach is assessed and compared with an argmax and a Monte Carlo Localization algorithm in terms of accuracy and computation time.
