Hill Climbing Algorithm for Random Sample Consensus Methods

We propose a modification of RANSAC that performs guided search of the sample space. The sampling is applicable to any of the sample consensus methods, such as MAPSAC or MLESAC. We give simulation results which show that the new method can reduce the number of required iterations to find a good model by orders of magnitude.

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