Congratulations to Guoxiang Zhang for Best Presentation Award @ ICCMA2020!
2020 The 8th International Conference on Control, Mechatronics and Automation will be held in Moscow, Russia on November 6-8, 2020. Due to COVID-19, this was an online event. Guoxiang presented a paper entitled "More Informed Random Sample Consensus." His presentation was clear, confident, convincing and on-time. He was award the best presentation award. Certificate is here.
The session chair spoke very highly on the contribution of this work. Congratulations!
Abstract—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 Lévy 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 Lévy 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.
Keywords-RANSAC; Lévy; point cloud registration
Created 11/16/2020. Last updated by Prof. YangQuan Chen