InforLorV4, Hal, Corpus, bibRecord, 002E71

Learning to automatically detect features for mobile robots using second-order Hidden Markov Models

Identifieur interne : 002E71 ( Hal/Corpus ); précédent : 002E70; suivant : 002E72

Learning to automatically detect features for mobile robots using second-order Hidden Markov Models

Auteurs : Olivier Aycard ; Jean-François Mari ; Richard Washington

Source :

RBID : Hal:inria-00071926

Abstract

In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automaticall- y detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

Url:

https://hal.inria.fr/inria-00071926

Links to Exploration step

Hal:inria-00071926

Le document en format XML

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<front><div type="abstract" xml:lang="en">In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automaticall- y detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.</div>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Learning to automatically detect features for mobile robots using second-order Hidden Markov Models</title>
<author role="aut"><persName><forename type="first">Olivier</forename>
<surname>Aycard</surname>
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<email>olivier.aycard@inria.fr</email>
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<author role="aut"><persName><forename type="first">Jean-François</forename>
<surname>Mari</surname>
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<author role="aut"><persName><forename type="first">Richard</forename>
<surname>Washington</surname>
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<date type="datePub">2002</date>
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<textClass><keywords scheme="author"><term xml:lang="da">sensor data interpretation</term>
<term xml:lang="da">hidden markov models</term>
<term xml:lang="da">mobile robots</term>
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<abstract xml:lang="en">In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automaticall- y detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.</abstract>
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Learning to automatically detect features for mobile robots using second-order Hidden Markov Models

Learning to automatically detect features for mobile robots using second-order Hidden Markov Models

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