Semi-autonomous Learning of an RFID Sensor Model for Mobile Robot Self-localization
In this paper, we present a method of learning a probabilistic RFID reader model with a mobile robot in a semi-automatic fashion. RFID and position data, recorded during an exploration phase, are used to learn the probability of detecting an RFID tag, for
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Summary. In this paper, we present a method of learning a probabilistic RFID reader model with a mobile robot in a semi-automatic fashion. RFID and position data, recorded during an exploration phase, are used to learn the probability of detecting an RFID tag, for which we investigate two non-parametric probability density estimation techniques. The trained model is finally used to localize the robot via a particle filter-based approach and optimized with respect to the resulting localization error. Experiments have shown that the learned models perform comparably well as a grid-based model learned from measurements in a stationary setup, but can be obtained easier.
1 Introduction Radio frequency identification (RFID) is nowadays not only used for identification purposes in the industry, but also for navigation tasks in mobile robotics. The technology allows for the contactless identification of objects and landmarks which are marked with RFID tags (also called labels or transponders) by a reader device and its antennas via radio waves. Passive tags obtain the energy for operation and response from the radio field of the RFID reader, which makes them inexpensive and easily maintainable. In case of passive UHF technology as in this work, however, factors such as the relative position of a tag and nearby materials affect the readability of a tag. Hence, in practice detection rates can be poor and noisy, and whatever application is regarded, it will benefit from an accurate model of tag detection probabilities. For example, the modeled detection field may lead to an improvement in the placement of RFID readers in a plant. Moreover, such a model is the basis of probabilistic localization algorithms. If it is easy to derive, it can be adapted or rebuilt quickly if the setup of the RFID system changes. In this paper, we present a method of learning a probabilistic RFID reader model with a mobile robot in a semi-automatic fashion. We have chosen a non-parametric approach, which means that we do not claim any specific functional form of the tag detection probability density. The approach should thus be applicable to other tasks, RFID standards, and hardware. A question which arises is how the quality of the learned model can be measured. We decided to plug it into the target application – the localization of the mobile robot in our case – and measure the resulting error there. Using RFID for selflocalization is motivated by the idea that, as more and more goods are being labeled
This work has been funded by the Landesstiftung Baden-W¨urttemberg within the scope of the support program BW-FIT and the research cooperation AmbiSense.
H. Bruyninckx et al. (Eds.): European Robotics Symposium 2008, STAR 44, pp. 273–282, 2008. c Springer-Verlag Berlin Heidelberg 2008 springerlink.com
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P. Vorst and A. Zell
with RFID tags and RFID hardware is getting cheaper, mobile robots operated e.g. in trade scenarios will be able to exploit RFID as a lower-cost sensor for self-localization and navigation in general. We proceed as fo
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