Automated Continuous Inventory Using Mobile Robots and Passive Long-Range RFID

Artur Koch

Radio Frequency Identification (RFID)

Due to its high potential in warehousing and logistics, RFID is becoming a more and more important alternative to the well-established bar code systems. On the one hand, RFID tags can easily be used as landmarks: Tag identifiers are unique and can be gathered remotely by radio waves at distances of several meters. On the other hand, RFID as a sensor technology is challenging: Measurements severely suffer from noise, and tags in theoretical read range (up to 10 meters) are frequently not observed.

Moreover, RFID readers do not report any spatial information (e.g., distances or bearings). We therefore research robust and efficient approaches which are able to

  • estimate the positions of tagged items
  • localize the robot

accurately and reliably in real time, despite the technical restrictions.

Fig. 1 RFID communication basics.

Development Platform

For development and evaluation, we use a MetraLabs Scitos G5 service robot. The mobile system is equipped with a SICK laser scanner for localization and ground truth pose estimation. The mounted RFID reader reports detected tags while the robot autonomously traverses the environment.

Fig. 2 MetraLabs Scitos G5 Service Robot.

Mapping of RFID Transponders

Since RFID does not provide any spatial information, we estimate the positions of detected tags using a sensor model based probabilistic approach utilizing Bayesian inference.

Fig. 3 2D detection rate model (left) and 3D received signal strength (RSS) model with mean (middle) and (right) at z=0.

We therefore, in a training stage, build sensor models that describe the likelihood to detect a tag given a position relative to the antenna for the utilized RFID device on the robot, with known pose, and reference tags, with known positions. Using the generated sensor models we are able to continously estimate the positions of goods in the environment online and in real time on the mobile platform. Recent results show, that by filtering out noise in form of reflected electromagnetic waves and utilizing a combined 2D detection rate as well as 3D RSS model, we are able to estimate unknown tag positions at a mean absolute mapping error of approx. 20cm. This enables our mobile system to precisely estimate the positions of unknown goods parallel to the often necessary inventorying process in the prior mentioned application scenarios.

Fingerprinting based Self-Localization

To further utilize RFID as a sensor on mobile robots, we investigate and develop approaches for RFID as an alternative to laser-based self-localization. Using RFID readings as landmarks makes it possible to estimate the current position of the mobile platform from reference measurements associated with locations in the environment. The developed applications do not require an explicit sensor model and are particularly efficient for mobile robots, since they are able to autonomously traverse and simultaneously annotate measurements from different sensors. Using the proposed approaches, our mobile platforms are able to efficiently estimate their pose with a mean abs. localization accuracy of 0.2-0.3m using RFID and odometry only.


Artur Koch
Tel.: +49 7071 29 70441
artur.koch (at)

Recent Publications

[1] Artur Koch and Andreas Zell. Mapping of passive UHF RFID tags with a mobile robot using outlier detection and negative information. In IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, May 2014. IEEE. [ details ]
[2] Ran Liu, Goran Huskić, and Andreas Zell. Dynamic Objects Tracking with a Mobile Robot using Passive UHF RFID Tags. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), Chicago, Illinois, USA, 2014. [ details ]
[3] Ran Liu and Andreas Zell. Towards Localizing Both Static and Non-static RFID Tags with a Mobile Robot. In International Conference on Intelligent Autonomous Systems (IAS-13), Padova, Italy, 2014. [ details ]
[4] Ran Liu, Artur Koch, and Andreas Zell. Mapping UHF RFID Tags with a Mobile Robot using 3D Sensor Model. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), Big Sight, Tokyo, Japan, November 2013. [ details | pdf ]
[5] Ran Liu, Artur Koch, and Andreas Zell. Path following with passive UHF RFID received signal strength in unknown environments. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), Vilamoura, Algarve, Portugal, October 2012. [ details | pdf ]
[6] Philipp Vorst, Artur Koch, and Andreas Zell. Efficient self-adjusting, similarity-based location fingerprinting with passive UHF RFID. In IEEE International Conference on RFID-Technology and Applications (RFID-TA2011), pages 160--167, Sitges, Barcelona, Spain, September 15-16 2011. IEEE. [ DOI | details | pdf ]
[7] Ran Liu, Philipp Vorst, Artur Koch, and Andreas Zell. Path following for indoor robots with RFID received signal strength. In The 19th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2011), Split, Hvar, and Dubrovnik, Croatia, September 2011. (Best paper award at the Symposium on RFID Technologies and Internet of Things). [ details | pdf ]
[8] Timo Schairer, Benjamin Huhle, Philipp Vorst, Andreas Schilling, and Wolfgang Strasser. Visual mapping with uncertainty for correspondence-free localization using Gaussian process regression. In Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, California, USA, September 2011. Accepted for publication.
[9] Philipp Vorst. Mapping, Localization, and Trajectory Estimation with Mobile Robots Using Long-Range Passive RFID. PhD thesis, University of Tuebingen, Tübingen, Germany, August 2011. [ details | link | pdf ]
[10] Philipp Vorst and Andreas Zell. A comparison of similarity measures for localization with passive RFID fingerprints. In ISR/ROBOTIK 2010 (Proceedings of the joint conference of ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics)), pages 354--361. VDE Verlag, June 2010. [ details | pdf ]
[11] Philipp Vorst and Andreas Zell. Fully autonomous trajectory estimation with long-range passive RFID. In 2010 IEEE International Conference on Robotics and Automation (ICRA), pages 1867--1872, Anchorage, Alaska, USA, May 2010. IEEE. [ DOI | details | pdf ]

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