A SLAM algorithm based on range and bearing estimation of passive UHF-RFID tags

In this paper we propose a Simultaneous Localization and Mapping (SLAM) algorithm for a mobile robot measuring the phase of the signal backscattered by a set of passive UHF-RFID tags, deployed in unknown position on the ceiling of the environment. The solution is based on a two level architecture. On a first (slave) level, a set of Multi-Hypothesis Extended Kalman Filters (MHEKF), one for each tag, estimates the range and the bearing of the tag with respect to the robot. On a second (master) level, the range and the bearing information of the responding tags is used in an EKF-SLAM algorithm which solves the SLAM problem. The proposed approach is more robust and computationally efficient with respect to other approaches available in the literature.