Abstract
We have already seen that the Extended Kalman Filter (EKF) is the most widely accepted tool for solving SLAM [31, 85]. One drawback however with the use of the EKF, is in the linear propagation of means and covariances. Vehicle and sensor models are usually of a very high nonlinear nature, and the effects of linearization required in the EKF can lead to filter divergence [53].
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Andrade-Cetto, J., Sanfeliu, A. Unscented Transformation of Vehicle States. In: Environment Learning for Indoor Mobile Robots. Springer Tracts in Advanced Robotics, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11418382_4
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DOI: https://doi.org/10.1007/11418382_4
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