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dc.contributor.authorTron, Roberto
dc.contributor.authorVidal, René
dc.date.accessioned2009-10-07T17:41:32Z
dc.date.available2009-10-07T17:41:32Z
dc.date.issued2009
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/33513
dc.description.abstractWe consider the problem of distributed estimation of the poses of N cameras in a camera sensor network using image measurements only. The relative rotation and translation (up to a scale factor) between pairs of neighboring cameras can be estimated using standard computer vision techniques. However, due to noise in the image measurements, these estimates may not be globally consistent. We address this problem by minimizing a cost function on S E (3)N in a distributed fashion using a generalization of the classical consensus algorithm for averaging Euclidean data. We also derive a condition for convergence, which relates the step-size of the consensus algorithm and the degree of the camera network graph. While our methods are designed with the camera sensor network application in mind, our results are applicable to other localization problems in a more general setting. We also provide synthetic simulations to test the validity of our approach.en
dc.description.sponsorshipThis work was supported by the grant NSF CNS-0834470en
dc.language.isoen_USen
dc.publisherDepartment of Electrical and Computer Engineering, Johns Hopkins Universityen
dc.subjectSensor networks
dc.subjectConsensus
dc.subjectLocalization
dc.subjectCamera calibration
dc.titleDistributed Image-Based 3-D Localization of Camera Sensor Networksen
dc.typeTechnical Reporten


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