Robot localization using WiFi signal without intensity map

Abstract

This paper describes a method to estimate the position of a mobile robot in an indoor scenario using the odometric calculus and the WiFi energy received from the wireless infrastructure. This energy will be measured by wireless network card on-board a mobile robot, and it will be used as another regular sensor to improve position estimation. The Bayes rule will be used to accumulate localization probability as the robot moves on. In this paper several experiments in a university building are shown. The two major contributions of the presented work are that the self-localization error achieved is bounded, and that no significant degradation is observed when the theoretical WiFi energy at each point is taken from radio propagation model instead of an a priori experimental intensity map of the environment.