A Supervised Learning Approach to Robot Localization Using a Short-Range RFID Sensor

Kanji TANAKA
Yoshihiko KIMURO
Kentaro YAMANO
Mitsuru HIRAYAMA
Eiji KONDO
Michihito MATSUMOTO

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E90-D    No.11    pp.1762-1771
Publication Date: 2007/11/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e90-d.11.1762
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence and Cognitive Science
Keyword: 
robot localization,  RFID,  Support Vector Machine,  landmark arrangement,  

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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.


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