Covariance recovery from a square root information matrix for data association

@article{Kaess2009CovarianceRF,
  title={Covariance recovery from a square root information matrix for data association},
  author={Michael Kaess and Frank Dellaert},
  journal={Robotics and Autonomous Systems},
  year={2009},
  volume={57},
  pages={1198-1210}
}
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… CONTINUE READING
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