Dual Monte Carlo Localization algorithm for localization using wireless signal strength maps

Gaussian Processes have been previously used to model wireless signals strengths and create locationsignal 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.