Semi-Autonomous Learning of an RFID Sensor Model for Mobile Robot Self-localization
P. Vorst, and A. Zell. European Robotics Symposium 2008, volume 44/2008 of Springer Tracts in Advanced Robotics, page 273-282. Springer Berlin / Heidelberg, (February 2008)
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.
%0 Conference Paper
%1 vorst2008sensor-model-learning
%A Vorst, Philipp
%A Zell, Andreas
%B European Robotics Symposium 2008
%D 2008
%E Siciliano, Bruno
%E Khatib, Oussama
%E Groen, Frans
%I Springer Berlin / Heidelberg
%K EPC RFID UHF detection long-range mobile model rate robot self-localization sensor
%P 273-282
%T Semi-Autonomous Learning of an RFID Sensor Model for Mobile Robot Self-localization
%U http://dx.doi.org/10.1007/978-3-540-78317-6_28
%V 44/2008
%X 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.
@inproceedings{vorst2008sensor-model-learning,
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.},
added-at = {2010-06-10T13:49:49.000+0200},
author = {Vorst, Philipp and Zell, Andreas},
biburl = {https://www.bibsonomy.org/bibtex/2216b1766b01d3c88aef5990fb6dfce04/fifo79},
booktitle = {European Robotics Symposium 2008},
editor = {Siciliano, Bruno and Khatib, Oussama and Groen, Frans},
interhash = {ad911ce2bce94001d4f5601e98d499a0},
intrahash = {216b1766b01d3c88aef5990fb6dfce04},
keywords = {EPC RFID UHF detection long-range mobile model rate robot self-localization sensor},
month = {February},
pages = {273-282},
publisher = {Springer Berlin / Heidelberg},
series = {Springer Tracts in Advanced Robotics},
timestamp = {2010-06-10T13:49:50.000+0200},
title = {Semi-Autonomous Learning of an RFID Sensor Model for Mobile Robot Self-localization},
url = {http://dx.doi.org/10.1007/978-3-540-78317-6_28},
volume = {44/2008},
year = 2008
}