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dc.contributorBielefeld University
dc.contributorLazaros Iliadis
dc.contributorChrisina Jayne
dc.contributorTC 12
dc.contributorWG 12.5
dc.contributor.authorBerghöfer, Elmar
dc.contributor.authorSchulze, Denis
dc.contributor.authorTscherepanow, Marko
dc.contributor.authorWachsmuth, Sven
dc.date.accessioned2019-10-25T03:12:23Z
dc.date.available2019-10-25T03:12:23Z
dc.date.created2017-08-14 23:04
dc.date.issued2011-09-15
dc.identifieroai:HAL:hal-01571378v1
dc.identifierhal-01571378
dc.identifierhttps://hal.inria.fr/hal-01571378
dc.identifierhttps://hal.inria.fr/hal-01571378/document
dc.identifierhttps://hal.inria.fr/hal-01571378/file/978-3-642-23957-1_1_Chapter.pdf
dc.identifier.urihttp://hdl.handle.net/20.500.12424/1267264
dc.description.abstractPart 1: Computer Vision and Robotics
dc.description.abstractInternational audience
dc.description.abstractRobots operating in complex environments shared with humans are confronted with numerous problems. One important problem is the identification of obstacles and interaction partners. In order to reach this goal, it can be beneficial to use data from multiple available sources, which need to be processed appropriately. Furthermore, such environments are not static. Therefore, the robot needs to learn novel objects. In this paper, we propose a method for learning and identifying obstacles based on multi-modal information. As this approach is based on Adaptive Resonance Theory networks, it is inherently capable of incremental online learning.
dc.languageen
dc.language.isoeng
dc.publisherHAL CCSD
dc.publisherSpringer
dc.rightshttp://creativecommons.org/licenses/by/
dc.sourceIFIP Advances in Information and Communication Technology
dc.subjectsensor data fusion
dc.subjectincremental learning
dc.subjectAdaptive Resonance Theory
dc.subject[INFO] Computer Science [cs]
dc.titleART-Based Fusion of Multi-modal Information for Mobile Robots
dc.typeinfo:eu-repo/semantics/conferenceObject
ge.collectioncodeOAIDATA
ge.dataimportlabelOAI metadata object
ge.identifier.legacyglobethics:10970837
ge.identifier.permalinkhttps://www.globethics.net/gel/10970837
ge.lastmodificationdate2017-08-14 23:04
ge.lastmodificationuseradmin@pointsoftware.ch (import)
ge.submissions0
ge.oai.exportid149104
ge.oai.repositoryid98398
ge.oai.setnameConference papers
ge.oai.setnameComputer Science [cs]
ge.oai.setnameIFIP-WG12-5
ge.oai.setnameTC12 - Artificial Intelligence
ge.oai.setnameEngineering Applications of Neural Networks
ge.oai.setnameIFIP-AIAI
ge.oai.setnameEngineering Applications of Neural Networks
ge.oai.setnameIFIP Advances in Information and Communication Technology
ge.oai.setnameIFIP - International Federation for Information Processing
ge.oai.setspectype:COMM
ge.oai.setspecsubject:info
ge.oai.setspeccollection:IFIP-WG12-5
ge.oai.setspeccollection:IFIP-TC12
ge.oai.setspeccollection:IFIP-EANN
ge.oai.setspeccollection:IFIP-AIAI
ge.oai.setspeccollection:IFIP-AICT-363
ge.oai.setspeccollection:IFIP-AICT
ge.oai.setspeccollection:IFIP
ge.oai.streamid2
ge.setnameGlobeEthicsLib
ge.setspecglobeethicslib
ge.linkhttps://hal.inria.fr/hal-01571378
ge.linkhttps://hal.inria.fr/hal-01571378/document
ge.linkhttps://hal.inria.fr/hal-01571378/file/978-3-642-23957-1_1_Chapter.pdf


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