Self-Localization with RFID Snapshots in Densely Tagged Environments
P. Vorst, S. Schneegans, B. Yang, and A. Zell. Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008), page 1353-1358. Nice, France, (September 2008)
Abstract
In this paper we show that, despite some disadvantageous properties of radio frequency identification (RFID), it is possible to localize a mobile robot quite accurately in environments which are densely tagged. We therefore employ a recently presented probabilistic fingerprinting technique called RFID snapshots. This method interprets short series of RFID measurements as feature vectors and is able to position a mobile robot after a training phase. It requires no explicit sensor model and is capable of exploiting given tag infrastructures, e.g., provided by supermarket shelves containing labeled products.
%0 Conference Paper
%1 vorst2008iros
%A Vorst, Philipp
%A Schneegans, Sebastian
%A Yang, Bin
%A Zell, Andreas
%B Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008)
%C Nice, France
%D 2008
%K RFID fingerprinting robot self-localization
%P 1353-1358
%T Self-Localization with RFID Snapshots in Densely Tagged Environments
%U http://www.ra.cs.uni-tuebingen.de/publikationen/2008/vorst2008iros_snapshots.pdf
%X In this paper we show that, despite some disadvantageous properties of radio frequency identification (RFID), it is possible to localize a mobile robot quite accurately in environments which are densely tagged. We therefore employ a recently presented probabilistic fingerprinting technique called RFID snapshots. This method interprets short series of RFID measurements as feature vectors and is able to position a mobile robot after a training phase. It requires no explicit sensor model and is capable of exploiting given tag infrastructures, e.g., provided by supermarket shelves containing labeled products.
@inproceedings{vorst2008iros,
abstract = {In this paper we show that, despite some disadvantageous properties of radio frequency identification (RFID), it is possible to localize a mobile robot quite accurately in environments which are densely tagged. We therefore employ a recently presented probabilistic fingerprinting technique called RFID snapshots. This method interprets short series of RFID measurements as feature vectors and is able to position a mobile robot after a training phase. It requires no explicit sensor model and is capable of exploiting given tag infrastructures, e.g., provided by supermarket shelves containing labeled products.},
added-at = {2008-10-13T10:57:29.000+0200},
address = {Nice, France},
author = {Vorst, Philipp and Schneegans, Sebastian and Yang, Bin and Zell, Andreas},
biburl = {https://www.bibsonomy.org/bibtex/227667a79d7e6f20e139304eb1b3611fb/fifo79},
booktitle = {Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008)},
interhash = {adbf71e2b2e3f51d3a27893dd27097a2},
intrahash = {27667a79d7e6f20e139304eb1b3611fb},
keywords = {RFID fingerprinting robot self-localization},
month = {September 22-26},
pages = {1353-1358},
timestamp = {2008-10-13T10:57:29.000+0200},
title = {Self-Localization with {RFID} Snapshots in Densely Tagged Environments},
url = {http://www.ra.cs.uni-tuebingen.de/publikationen/2008/vorst2008iros_snapshots.pdf},
year = 2008
}