Localization in a vector field map

Localization using continuous signals such as WiFi or active beacons is a cost-effective approach for enabling systematic navigation of robots. In our previous work we showed how localization maps, represented as regular grids, of such signals can be learned through application of vector field SLAM [1]. In this paper we describe a method that, given such a localization map, finds the pose of a mobile robot from observations of the signals. Our method first generates pose hypotheses by searching the localization map for places that best fit to a measurement taken by the robot. A localization filter using an extended Kalman filter (EKF) then verifies one pose hypothesis by tracking the pose over a short distance. In experiments carried out in a standard test environment equipped with active beacons we obtain an average position accuracy of 10 to 35 cm with a localization success rate of 96 to 99 %. The proposed method enables a robot mapping an environment using vector field SLAM to recover from kidnapping and resume its navigation.