This paper presents a method for reducing the computational complexity of Kalman Filters where large numbers of dimensions have no process noise (e.g. in SLAM). The method reduces the dimensionality of the filter by removing dimensions which have been accurately measured, retaining just the unknown dimensions in the filter.
[BMVC 2013 paper]
[BMVC 2013 paper]
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