Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-02713-0_30
DC FieldValue
dc.titleRobust pose estimation for outdoor mixed reality with sensor fusion
dc.contributor.authorZhou, Z.
dc.contributor.authorKarlekar, J.
dc.contributor.authorHii, D.
dc.contributor.authorSchneider, M.
dc.contributor.authorLu, W.
dc.contributor.authorWittkopf, S.
dc.date.accessioned2013-10-14T03:08:19Z
dc.date.available2013-10-14T03:08:19Z
dc.date.issued2009
dc.identifier.citationZhou, Z.,Karlekar, J.,Hii, D.,Schneider, M.,Lu, W.,Wittkopf, S. (2009). Robust pose estimation for outdoor mixed reality with sensor fusion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5616 LNCS (PART 3) : 281-289. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-02713-0_30" target="_blank">https://doi.org/10.1007/978-3-642-02713-0_30</a>
dc.identifier.isbn3642027121
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/45527
dc.description.abstractWe present a sensor fusion based technique for outdoor augmented reality system for mobile devices using GPS, gyroscope, and geo-referenced 3D models of the urban environment. Geo-spatial interaction not only provides overlays of the existing environment but compliments with other data such as location-specific photos, videos and other information from different time periods enhancing the overall user experience of augmented reality. To provide robust pose estimation of the camera relative to the world coordinates, firstly, GPS and gyroscope are used to obtain the rough estimation. Secondly, model based silhouette tracking and sensor fusion approach is used to refine the rough estimation and to provide seamless media rich augmentation of 3D textured models. © 2009 Springer Berlin Heidelberg.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-02713-0_30
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentARCHITECTURE
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/978-3-642-02713-0_30
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5616 LNCS
dc.description.issuePART 3
dc.description.page281-289
dc.identifier.isiutNOT_IN_WOS
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