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Vision Based Localisation for a Mobile Robot

Identifieur interne : 001A07 ( Crin/Checkpoint ); précédent : 001A06; suivant : 001A08

Vision Based Localisation for a Mobile Robot

Auteurs : Franck Gechter ; François Charpillet

Source :

RBID : CRIN:gechter00a

English descriptors

Abstract

We present a vision based localization system for a mobile robot that uses a representative set of images obtained during an initial exploration of the environment. This set of images makes it possible to represent the environment as a partially markov decision process. The originality of this approach is the resulting data fusion process that uses both image matching and decision made by the robot in order to estimate the set of plausible position of the robot and the associated probabilities. Image matching or recognition is achieved using principal components analysis.

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Le document en format XML

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<div type="abstract" xml:lang="en" wicri:score="2353">We present a vision based localization system for a mobile robot that uses a representative set of images obtained during an initial exploration of the environment. This set of images makes it possible to represent the environment as a partially markov decision process. The originality of this approach is the resulting data fusion process that uses both image matching and decision made by the robot in order to estimate the set of plausible position of the robot and the associated probabilities. Image matching or recognition is achieved using principal components analysis.</div>
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<BibTex type="inproceedings">
<ref>gechter00a</ref>
<crinnumber>A00-R-232</crinnumber>
<category>3</category>
<equipe>MAIA</equipe>
<author>
<e>Gechter, Franck</e>
<e>Charpillet, François</e>
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<title>Vision Based Localisation for a Mobile Robot</title>
<booktitle>{International Conference on Tools with Artificial Intelligence}</booktitle>
<year>2000</year>
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<abstract>We present a vision based localization system for a mobile robot that uses a representative set of images obtained during an initial exploration of the environment. This set of images makes it possible to represent the environment as a partially markov decision process. The originality of this approach is the resulting data fusion process that uses both image matching and decision made by the robot in order to estimate the set of plausible position of the robot and the associated probabilities. Image matching or recognition is achieved using principal components analysis.</abstract>
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