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

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