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dc.contributor.authorCarlone, Luca
dc.contributor.authorKira, Zsolt
dc.contributor.authorBeall, Chris
dc.contributor.authorIndelman, Vadim
dc.contributor.authorDellaert, Frank
dc.date.accessioned2015-08-10T20:19:14Z
dc.date.available2015-08-10T20:19:14Z
dc.date.issued2014
dc.identifier.citationCarlone, L.; Kira, Z.; Beall, C.; Indelman, V.; & Dellaert, F. (2014). "Eliminating Conditionally Independent Sets in Factor Graphs: A Unifying Perspective Based on Smart Factors". IEEE International Conference on Robotics and Automation (ICRA 2014), May 31 2014-June 7 2014, pp. 4290-4297.en_US
dc.identifier.urihttp://hdl.handle.net/1853/53717
dc.description© 2014 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.descriptionDOI: 10.1109/ICRA.2014.6907483
dc.description.abstractFactor graphs are a general estimation framework that has been widely used in computer vision and robotics. In several classes of problems a natural partition arises among variables involved in the estimation. A subset of the variables are actually of interest for the user: we call those target variables. The remaining variables are essential for the formulation of the optimization problem underlying maximum a posteriori (MAP) estimation; however these variables, that we call support variables, are not strictly required as output of the estimation problem. In this paper, we propose a systematic way to abstract support variables, defining optimization problems that are only defined over the set of target variables. This abstraction naturally leads to the definition of smart factors, which correspond to constraints among target variables. We show that this perspective unifies the treatment of heterogeneous problems, ranging from structureless bundle adjustment to robust estimation in SLAM. Moreover, it enables to exploit the underlying structure of the optimization problem and the treatment of degenerate instances, enhancing both computational efficiency and robustness.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectBundle adjustmenten_US
dc.subjectFactor graphen_US
dc.subjectMaximum a posteriorien_US
dc.subjectSimultaneous localization and mappingen_US
dc.titleEliminating Conditionally Independent Sets in Factor Graphs: A Unifying Perspective based on Smart Factorsen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute 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.contributor.corporatenameGeorgia Tech Research Instituteen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.identifier.doi10.1109/ICRA.2014.6907483
dc.embargo.termsnullen_US


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