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Erschienen in:
2022 | OriginalPaper | Buchkapitel
Research on Feature Matching Based on Improved RANSAC Algorithm
verfasst von : Xianfeng Wang, Baitong Wang, Zilin Ding, Tong Zhao
Erschienen in: Intelligent Robotics and Applications
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Abstract
Aiming at the problems that the current RANSAC (Random Sample Consensus) algorithm has too large randomness and is susceptible to external point interference, which leads to the reduction of matching accuracy, an improved RANSAC algorithm combining feature matching confidence and grid clustering is proposed. Firstly, rough matching is carried out by the FLANN algorithm, and confidence analysis is carried out on the coarse matching point pairs, then expanding grid clustering around the high confidence point pairs. Multiple local optimal interior points are screened to optimize the global interior points and improve the matching accuracy of feature points. The experimental results show that the improved RANSAC in this paper increases the existence probability of interior points, avoids too many wrong feature matching affecting the model effect of the homography matrix, and improves the accuracy of feature matching.