A Supervised Learning Approach to Robot Localization Using a Short-Range RFID Sensor
Summary :
This work is concerned with the problem of robot localization using standard RFID tags as landmarks and an RFID reader as a landmark sensor. A main advantage of such an RFID-based localization system is the availability of landmark ID measurement, which trivially solves the data association problem. While the main drawback of an RFID system is its low spatial accuracy. The result in this paper is an improvement of the localization accuracy for a standard short-range RFID sensor. One of the main contributions is a proposal of a machine learning approach in which multiple classifiers are trained to distinguish RFID-signal features of each location. Another contribution is a design tool for tag arrangement by which the tag configuration needs not be manually designed by the user, but can be automatically recommended by the system. The effectiveness of the proposed technique is evaluated experimentally with a real mobile robot and an RFID system.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.11 pp.1762-1771
- Publication Date
- 2007/11/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.11.1762
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence and Cognitive Science
Authors
Keyword
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Kanji TANAKA, Yoshihiko KIMURO, Kentaro YAMANO, Mitsuru HIRAYAMA, Eiji KONDO, Michihito MATSUMOTO, "A Supervised Learning Approach to Robot Localization Using a Short-Range RFID Sensor" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 11, pp. 1762-1771, November 2007, doi: 10.1093/ietisy/e90-d.11.1762.
Abstract: This work is concerned with the problem of robot localization using standard RFID tags as landmarks and an RFID reader as a landmark sensor. A main advantage of such an RFID-based localization system is the availability of landmark ID measurement, which trivially solves the data association problem. While the main drawback of an RFID system is its low spatial accuracy. The result in this paper is an improvement of the localization accuracy for a standard short-range RFID sensor. One of the main contributions is a proposal of a machine learning approach in which multiple classifiers are trained to distinguish RFID-signal features of each location. Another contribution is a design tool for tag arrangement by which the tag configuration needs not be manually designed by the user, but can be automatically recommended by the system. The effectiveness of the proposed technique is evaluated experimentally with a real mobile robot and an RFID system.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.11.1762/_p
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@ARTICLE{e90-d_11_1762,
author={Kanji TANAKA, Yoshihiko KIMURO, Kentaro YAMANO, Mitsuru HIRAYAMA, Eiji KONDO, Michihito MATSUMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={A Supervised Learning Approach to Robot Localization Using a Short-Range RFID Sensor},
year={2007},
volume={E90-D},
number={11},
pages={1762-1771},
abstract={This work is concerned with the problem of robot localization using standard RFID tags as landmarks and an RFID reader as a landmark sensor. A main advantage of such an RFID-based localization system is the availability of landmark ID measurement, which trivially solves the data association problem. While the main drawback of an RFID system is its low spatial accuracy. The result in this paper is an improvement of the localization accuracy for a standard short-range RFID sensor. One of the main contributions is a proposal of a machine learning approach in which multiple classifiers are trained to distinguish RFID-signal features of each location. Another contribution is a design tool for tag arrangement by which the tag configuration needs not be manually designed by the user, but can be automatically recommended by the system. The effectiveness of the proposed technique is evaluated experimentally with a real mobile robot and an RFID system.},
keywords={},
doi={10.1093/ietisy/e90-d.11.1762},
ISSN={1745-1361},
month={November},}
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TY - JOUR
TI - A Supervised Learning Approach to Robot Localization Using a Short-Range RFID Sensor
T2 - IEICE TRANSACTIONS on Information
SP - 1762
EP - 1771
AU - Kanji TANAKA
AU - Yoshihiko KIMURO
AU - Kentaro YAMANO
AU - Mitsuru HIRAYAMA
AU - Eiji KONDO
AU - Michihito MATSUMOTO
PY - 2007
DO - 10.1093/ietisy/e90-d.11.1762
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2007
AB - This work is concerned with the problem of robot localization using standard RFID tags as landmarks and an RFID reader as a landmark sensor. A main advantage of such an RFID-based localization system is the availability of landmark ID measurement, which trivially solves the data association problem. While the main drawback of an RFID system is its low spatial accuracy. The result in this paper is an improvement of the localization accuracy for a standard short-range RFID sensor. One of the main contributions is a proposal of a machine learning approach in which multiple classifiers are trained to distinguish RFID-signal features of each location. Another contribution is a design tool for tag arrangement by which the tag configuration needs not be manually designed by the user, but can be automatically recommended by the system. The effectiveness of the proposed technique is evaluated experimentally with a real mobile robot and an RFID system.
ER -
English
