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dc.contributor.authorKaess, Michael
dc.contributor.authorDellaert, Frank
dc.date.accessioned2011-03-25T19:48:26Z
dc.date.available2011-03-25T19:48:26Z
dc.date.issued2009
dc.identifier.citationKaess, M., & Dellaert, F. “Covariance Recovery from a Square Root Information Matrix for Data Association”. Robotics and Autonomous Systems, Vol. 57, no. 12 (2009), 1198.en_US
dc.identifier.issn0921-8890
dc.identifier.urihttp://hdl.handle.net/1853/38287
dc.descriptionDOI: 10.1016/j.robot.2009.06.008.en_US
dc.description.abstractData association is one of the core problems of simultaneous localization and mapping (SLAM), and it requires knowledge about the uncertainties of the estimation problem in the form of marginal covariances. However, it is often difficult to access these quantities without calculating the full and dense covariance matrix, which is prohibitively expensive. We present a dynamic programming algorithm for efficient recovery of the marginal covariances needed for data association. As input we use a square root information matrix as maintained by our incremental smoothing and mapping (iSAM) algorithm. The contributions beyond our previous work are an improved algorithm for recovering the marginal covariances and a more thorough treatment of data association now including the joint compatibility branch and bound (JCBB) algorithm. We further show how to make information theoretic decisions about measurements before actually taking the measurement, therefore allowing a reduction in estimation complexity by omitting uninformative measurements. We evaluate our work on simulated and real-world data.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectData associationen_US
dc.subjectSimultaneous localization and mappingen_US
dc.subjectSmoothingen_US
dc.titleCovariance Recovery from a Square Root Information Matrix for Data Associationen_US
dc.typePre-printen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing
dc.contributor.corporatenameMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.publisher.originalElsevier


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