Semi-Autonomous Learning of an RFID Sensor Model for Mobile Robot Self-Localization Philipp Vorst and Andreas Zell Department of Computer Science, University of T¨ ubingen, T¨ ubingen, Germany ⋆ {philipp.vorst,andreas.zell}@uni-tuebingen.de 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 posi- tion 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 local- ization 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 identifica- tion purposes in the industry, but also for navigation tasks in mobile robotics. The technology allows for the contactless identification of objects and land- marks 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 posi- tion 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. ⋆ 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.