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dc.contributor.authorErdem, Tanju
dc.contributor.authorErcan, Ali Özer
dc.date.accessioned2015-10-23T05:33:13Z
dc.date.available2015-10-23T05:33:13Z
dc.date.issued2015-02
dc.identifier.issn1057-7149
dc.identifier.urihttp://hdl.handle.net/10679/947
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6983575
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.en_US
dc.description.abstractIn a setup where camera measurements are used to estimate 3D egomotion in an extended Kalman filter (EKF) framework, it is well-known that inertial sensors (i.e., accelerometers and gyroscopes) are especially useful when the camera undergoes fast motion. Inertial sensor data can be fused at the EKF with the camera measurements in either the correction stage (as measurement inputs) or the prediction stage (as control inputs). In general, only one type of inertial sensor is employed in the EKF in the literature, or when both are employed they are both fused in the same stage. In this paper, we provide an extensive performance comparison of every possible combination of fusing accelerometer and gyroscope data as control or measurement inputs using the same data set collected at different motion speeds. In particular, we compare the performances of different approaches based on 3D pose errors, in addition to camera reprojection errors commonly found in the literature, which provides further insight into the strengths and weaknesses of different approaches. We show using both simulated and real data that it is always better to fuse both sensors in the measurement stage and that in particular, accelerometer helps more with the 3D position tracking accuracy, whereas gyroscope helps more with the 3D orientation tracking accuracy. We also propose a simulated data generation method, which is beneficial for the design and validation of tracking algorithms involving both camera and inertial measurement unit measurements in general.en_US
dc.description.sponsorshipTÜBİTAK
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relationinfo:turkey/grantAgreement/TUBITAK/110E053en_US
dc.relation.ispartofIEEE Transactions on Image Processing
dc.rightsopenAccess
dc.titleFusing inertial sensor data in an extended kalman filter for 3D camera trackingen_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-8841-1642 & YÖK ID 45777) Erdem, Tanju
dc.contributor.authorID(ORCID 0000-0003-1126-8259 & YÖK ID 35788) Ercan, Ali
dc.contributor.ozuauthorErdem, Tanju
dc.contributor.ozuauthorErcan, Ali Özer
dc.identifier.volume24
dc.identifier.issue2
dc.identifier.startpage538
dc.identifier.endpage548
dc.identifier.wosWOS:000354550200003
dc.identifier.doi10.1109/TIP.2014.2380176
dc.subject.keywordsInertial sensor fusionen_US
dc.subject.keywordsExtended Kalman filteren_US
dc.subject.keywords3D camera trackingen_US
dc.subject.keywordsinertial measurement uniten_US
dc.subject.keywordsAccelerometeren_US
dc.subject.keywordsGyroscopeen_US
dc.identifier.scopusSCOPUS:2-s2.0-84920972626
dc.contributor.authorMale2
dc.relation.publicationcategoryArticle - International Refereed Journal - Institutional Academic Staff


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