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I know that there is an extended kalman filter approach to simultaneous localization and mapping. I'm curious if there is a SLAM algorithm that exploits the ensemble kalman filter. A citation would be great, if at all possible.

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Well let's see. The original paper has approximately 3,000 citations in various branches of literature, including numerous applied textbooks. I'd say this question lacks research, but given the large amount of follow-up literature, I understand how you may not be able to find a SLAM-specific implementation.

I was able to find many papers using enKF/enEKF for visual applications, but relatively few for true map-based SLAM. (I think the term SLAM is over-used and over-specific, but that's an aside.) If you relax your definition of a map to be any feature tracked across multiple sensor measurements, then you should also include:

  • Vehicle to vehicle localization.
  • Visual feature tracking
  • Target tracking from emplaced sensor measurements
  • Self localization and state estimation

But, doing a search for "SLAM" in the context of directly citing the enKF article yielded only the following: State estimation of connected vehicles using a nonlinear ensemble filter

This does not preclude enKF-SLAM since my search was restrictive.

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Did you check out this website?http://enkf.nersc.no/ It seems that the authors have an extended literature on enKF and examples in Matlab and Fortran are provided.

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  • $\begingroup$ There is no enKF SLAM algorithm in the website you linked. $\endgroup$ – Paul Jan 4 '16 at 17:02

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