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GMapping is highly efficient Rao-Blackwellized particle filter for learning grid maps.

The ideas are
(1) to draw the particles from an improved proposal, computed according to the most recent observation, and
(2) To resample whenever an indicator of the variance of the weights is below a given threshold.
The first allows for drawing the particles in a close position to the true posterior, while the second reduces the particle uncertainly. The sourcecode as well as several corrected and uncorrected datasets are available.



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