Philipp Vorst, Andreas Zell

Semi-Autonomous Learning of an RFID Sensor Model for Mobile Robot Self-localization

European Robotics Symposium 2008, volume 44/2008 of Springer Tracts in Advanced Robotics, Springer Berlin/Heidelberg, 2008, pp. 273-282

Abstract

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.

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Semi-Autonomous Learning of an RFID Sensor Model for Mobile Robot Self-localization
Poster presented at EUROS 2008

BibTeX 

@INPROCEEDINGS{vorst2008sensor-model-learning,
  author      = {Philipp Vorst and Andreas Zell},
  title       = {Semi-Autonomous Learning of an RFID Sensor Model 
                 for Mobile Robot Self-localization},
  booktitle   = {European Robotics Symposium 2008},
  year        = {2008},
  editor      = {Bruno Siciliano and Oussama Khatib and Frans Groen},
  volume      = {44/2008},
  series      = {Springer Tracts in Advanced Robotics},
  pages       = {273--282},
  month       = {February},
  publisher   = {Springer Berlin/Heidelberg},
  doi         = {10.1007/978-3-540-78317-6},
}