WiFi localization methods for autonomous robots

This paper compares two methods to estimate the position of a mobile robot in an indoor environment using only odometric calculus and the WiFi energy received from the wireless communication infrastructure. In both cases we use a well-known probabilistic method based on the Bayes rule to accumulate localization probability as the robot moves on with an experimental WiFi map, and with a theoretically built WiFi map. We will show several experiments made in our university building to compare both methods using a Pioneer robot. The two major contributions of the presented work are that the self-localization error achieved with WiFi energy is bounded, and that no significant degradation is observed when the expected WiFi energy at each point is taken from radio propagation model, instead of an a priori experimental intensity map of the environment.