Abstract-In this paper we show that, despite some disadvantageous properties of radio frequency identification (RFID), it is possible to localize a mobile robot quite accurately in environments which are densely tagged. We therefore employ a recently presented probabilistic fingerprinting technique called RFID snapshots. This method interprets short series of RFID measurements as feature vectors and is able to position a mobile robot after a training phase. It requires no explicit sensor model and is capable of exploiting given tag infrastructures, e.g., provided by supermarket shelves containing labeled products. Radio frequency identification (RFID) has found its way into robotics because of advantageous properties such as the unique identification of RFID tags and the resulting unambiguous association of sensor readings with landmarks. Moreover, since RFID reader and tags (also called transponders) communicate via electromagnetic waves, line of sight is not required between them. On the other hand, especially passive ultra-high frequency (UHF, 868/915 MHz) RFID imposes some difficulties: First, RFID readers of this type only report which tags have been detected; they neither know the distance nor the bearing to a detected tag (and do not even report signal strength information). Second, the successful detection of a tag within the read range of up to 7 m depends largely on factors such as the relative position of the tag with respect to the reader antenna as well as on the materials of nearby objects. Hence, today it is widely assumed that the use of RFID for self-localization is disadvantageous due to sensor noise and potentially low positioning accuracy. In this paper, however, we show that it is possible to localize a mobile robot via RFID and odometry alone at an accuracy of less than 0.3 m. We present empirical results derived from extensive experiments with a mobile robot and off-the-shelf RFID hardware. Our approach is based on a fingerprinting technique called RFID snapshots, which we presented recently [8] . In this method, short series of RFID measurements are interpreted as feature vectors. They can be trained at reference positions during an offline phase and later be used to estimate the pose of the robot corresponding to current tag detections. A particle filter is employed in order to improve accuracy and robustness. The advantages of our approach are that no explicit sensor model is required and that environment characteristics are implicitly learned by the
REF present a mobile object localisation approach using reference tags, onboard readers, and a particle filter-based technique.
Self-Localization with RFID snapshots in densely tagged environments
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science