Loop closure and trajectory estimation with long-range passive RFID in densely tagged environments

In more and more commercial scenarios, radio frequency identification (RFID) is used to tag assets on a large scale. These given tag infrastructures offer themselves for the navigation of autonomous transport vehicles and service robots. In this paper we investigate loop closure for graph-based simultaneous localization and mapping (SLAM) and trajectory estimation in environments with such dense RFID infrastructures: We compare different methods of inferring that a place has been revisited, examine their robustness, and show how the trajectory of the robot can be reconstructed. Given this trajectory, a robot is able to map transponder positions or to localize itself with RFID and odometry alone and without a reference localization system. The accuracy of our approach is shown through a series of experiments with a mobile robot.