Random sample consensus (RANSAC) is a robust model-fitting algorithm. It is widely used in many fields including image-stitching and point cloud registration. In RANSAC, data is uniformly sampled for hypothesis generation. However, this uniform sampling strategy does not fully utilize all the information on many problems. In this paper, we propose a method that samples data with a L{e}vy distribution together with a data sorting algorithm. In the hypothesis sampling step of the proposed method, data is sorted with a sorting algorithm we proposed, which sorts data based on the likelihood of a data point being in the inlier set. Then, hypotheses are sampled from the sorted data with L{e}vy distribution. The proposed method is evaluated on both simulation and real-world public datasets. Our method shows better results compared with the uniform baseline method.
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