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dc.contributor.authorIndelman, Vadim
dc.contributor.authorRoberts, Richard
dc.contributor.authorDellaert, Frank
dc.date.accessioned2015-08-19T19:48:56Z
dc.date.available2015-08-19T19:48:56Z
dc.date.issued2013-01
dc.identifier.citationIndelman, V.; Roberts, R.; & Dellaert, F. (2013). "Probabilistic Analysis of Incremental Light Bundle Adjustment." 2013 IEEE Workshop on Robot Vision (WORV 2013), 15-17 January 2013, pp. 221-228.en_US
dc.identifier.isbn978-1-4673-5646-6
dc.identifier.urihttp://hdl.handle.net/1853/53764
dc.description© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_US
dc.description2013 IEEE Workshop on Robot Vision (WORV 2013), 15-17 January 2013, Clearwater Beach, FL.
dc.descriptionDOI: 10.1109/WORV.2013.6521942
dc.description.abstractThis paper presents a probabilistic analysis of the recently introduced incremental light bundle adjustment method (iLBA) [6]. In iLBA, the observed 3D points are algebraically eliminated, resulting in a cost function with only the camera poses as variables, and an incremental smoothing technique is applied for efficiently processing incoming images. While we have already showed that compared to conventional bundle adjustment (BA), iLBA yields a significant improvement in computational complexity with similar levels of accuracy, the probabilistic properties of iLBA have not been analyzed thus far. In this paper we consider the probability distribution that corresponds to the iLBA cost function, and analyze how well it represents the true density of the camera poses given the image measurements. The latter can be exactly calculated in bundle adjustment (BA) by marginalizing out the 3D points from the joint distribution of camera poses and 3D points. We present a theoretical analysis of the differences in the way that LBA and BA use measurement information. Using indoor and outdoor datasets we show that the first two moments of the iLBA and the true probability distributions are very similar in practice.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectBundle adjustmenten_US
dc.subjectComputational complexityen_US
dc.subjectFactor graphen_US
dc.subjectiLBAen_US
dc.subjectImage observationsen_US
dc.subjectIncremental light bundle adjustmenten_US
dc.subjectIncremental smoothingen_US
dc.subjectProbabilistic analysisen_US
dc.subject3D pointsen_US
dc.titleProbabilistic Analysis of Incremental Light Bundle Adjustmenten_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computingen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.identifier.doi10.1109/WORV.2013.6521942
dc.embargo.termsnullen_US


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