Abstract
Point set registration, which aims to seek correspondence between multiple point sets, is an important and key technique in image processing and point cloud processing. However, the current point set registration algorithms still have many shortcomings, such as the sensitivity to the initial position and high computation load. In this paper, a novel method is proposed to deal with this problem, called RPR-GMC. Different from the traditional point-to-point correspondence, the proposed algorithm uses the Gaussian mixture clustering method, and fits the Gaussian mixture probability distributions to the data point set through the maximum likelihood. The proposed algorithm improves the sensitivity to initial position and reduces the time complexity. Compare with current the most advanced methods, RPR-GMC shows better accuracy and lower computation load, especially in large-scale data.
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