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dc.contributor.authorRuiz-Sarmiento, José Raúl 
dc.contributor.authorGuenther, Martin
dc.contributor.authorGalindo-Andrades, Cipriano 
dc.contributor.authorGonzález-Jiménez, Antonio Javier 
dc.contributor.authorHertzberg, Joachim
dc.date.accessioned2017-03-31T10:00:56Z
dc.date.available2017-03-31T10:00:56Z
dc.date.created2017
dc.date.issued2017-03-31
dc.identifier.urihttp://hdl.handle.net/10630/13408
dc.description.abstractThis work proposes a robotic object recognition system that takes advantage of the contextual information latent in human-like environments in an online fashion. To fully leverage context, it is needed perceptual information from (at least) a portion of the scene containing the objects of interest, which could not be entirely covered by just an one-shot sensor observation. Information from a larger portion of the scenario could still be considered by progressively registering observations, but this approach experiences difficulties under some circumstances, e.g. limited and heavily demanded computational resources, dynamic environments, etc. Instead of this, the proposed recognition system relies on an anchoring process for the fast registration and propagation of objects’ features and locations beyond the current sensor frustum. In this way, the system builds a graphbased world model containing the objects in the scenario (both in the current and previously perceived shots), which is exploited by a Probabilistic Graphical Model (PGM) in order to leverage contextual information during recognition. We also propose a novel way to include the outcome of local object recognition methods in the PGM, which results in a decrease in the usually high CRF learning complexity. A demonstration of our proposal has been conducted employing a dataset captured by a mobile robot from restaurant-like settings, showing promising results.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectRobots autónomoses_ES
dc.subject.otherMobile roboticses_ES
dc.subject.otherObject recognitiones_ES
dc.subject.otherSemanticses_ES
dc.titleOnline Context-based Object Recognition for Mobile Robotses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitle17th International Conference on Autonomous Robot Systems and Competition (ICARSC)es_ES
dc.relation.eventplaceCoimbraes_ES
dc.relation.eventdate26/04/2017es_ES
dc.cclicenseby-nc-ndes_ES
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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