Kalman filtering for sensor fusion in a human tracking system

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/14233
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dc.contributorAutomática, Robótica y Visión Artificialen
dc.contributor.authorCorrales Ramón, Juan Antonio-
dc.contributor.authorCandelas-Herías, Francisco A.-
dc.contributor.authorTorres, Fernando-
dc.contributor.otherUniversidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señalen
dc.date.accessioned2010-07-12T07:25:28Z-
dc.date.available2010-07-12T07:25:28Z-
dc.date.created2010-05-01-
dc.date.issued2010-05-01-
dc.identifier.citationCORRALES RAMÓN, Juan Antonio; CANDELAS HERÍAS, Francisco Andrés; TORRES MEDINA, Fernando. "Kalman filtering for sensor fusion in a human tracking system". En: Kalman Filter / Edited by Vedran Kordic. Vukovar, Croatia : INTECH, 2010. ISBN 978-953-307-094-0, pp. 59-72en
dc.identifier.isbn978-953-307-094-0-
dc.identifier.urihttp://hdl.handle.net/10045/14233-
dc.description.abstractThis chapter presents a human tracking system for developing human-robot interaction tasks. This system is composed by two subsystems: an inertial motion capture system and a Ultra-WideBand (UWB) localization system. Two fusion algorithms are used to combine the measurements of both systems. The first fusion algorithm transforms measurements from the two systems in the same coordinate system by recalculating the transformation matrix each time a new measurement from the UWB system is received. This approach relies heavily on the accuracy of the measurements from the UWB system because the transformation matrix recalculation assumes that the last UWB measurement is completely correct. Thus, errors in UWB measurements are not considered and only the translational errors of the motion capture system are corrected. The second algorithm takes into account UWB errors and overcomes the drawbacks of the first approach by adding a modified Kalman filter.en
dc.description.sponsorshipThis work is supported by the Spanish Ministry of Education and Science (MEC) under the research project DPI2005-06222 ('Design, Implementation and Experimentation of Intelligent Manipulation Scenarios for Automatic Assembly and Disassembly Applications') and the pre-doctoral grant AP2005-1458.en
dc.languageengen
dc.publisherINTECHen
dc.publisherSciyoen
dc.subjectKalman filteren
dc.subjectSensor fusionen
dc.subjectMotion captureen
dc.subjectHuman-robot interactionen
dc.subjectHuman trackingen
dc.subjectUWB localizationen
dc.subject.otherIngeniería de Sistemas y Automáticaen
dc.titleKalman filtering for sensor fusion in a human tracking systemen
dc.typeinfo:eu-repo/semantics/bookParten
dc.peerreviewedsien
dc.relation.publisherversionhttp://sciyo.com/articles/show/title/kalman-filtering-for-sensor-fusion-in-a-human-tracking-systemen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
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