Covariance Recovery from a Square Root Information Matrix for Data Association

Show simple item record Kaess, Michael Dellaert, Frank 2011-03-25T19:48:26Z 2011-03-25T19:48:26Z 2009
dc.identifier.citation Kaess, 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.issn 0921-8890
dc.description DOI: 10.1016/j.robot.2009.06.008. en_US
dc.description.abstract Data 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.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Data association en_US
dc.subject Simultaneous localization and mapping en_US
dc.subject Smoothing en_US
dc.title Covariance Recovery from a Square Root Information Matrix for Data Association en_US
dc.type Pre-print en_US
dc.contributor.corporatename Georgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatename Georgia Institute of Technology. College of Computing
dc.contributor.corporatename Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.publisher.original Elsevier

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