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

Abstract

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 graphbased 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.