RANSAC for RANdom SAmple Consensus, it is based on a set of sample data sets contain abnormal data, mathematical model parameters calculated data, the effective sample data algorithm. First proposed by Fischler and Bolles in 1981 [1]. The RANSAC algorithm often used in computer vision. For example, while addressing the three-dimensional visual field on the camera match point problem and the fundamental matrix calculation. The RANSAC algorithm' s basic assumption is that sample contains the correct data (inliers, can be described by the model data) also contain abnormal data (Outliers, deviated from the normal range very far, unable to adapt to the mathematical model of the data), that the data set contains noise. These abnormal data may be generated due to wrong measurements, wrong assumptions, wrong calculation. Simultaneously RANSAC also assume, given a set of correct data, there can be calculated out of these data to the model parameters.

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