Probability Guided Random Sample Consensus

In this paper a probability guided Random Sample Consensus algorithm is proposed in order to improve the computational efficiency of the traditional Random Sample Consensus．The weight of each sample is dynamically adjusted during the sampling process by measuring the errors of the estimated model on original data set,which enhances the probability of sampling a correct subset． The iteration process of sampling and model updating forms a positive feedback after a good subset is sampled．The inlier samples will be sampled with a much larger probability than outlier samples after several iterations．The theoretical analysis and experimental results show that our proposed algorithm can obtain a good model estimation with less sampling times compared with the traditional Random Sample Consensus algorithm．