改进型SIFT立体匹配算法研究

Research on improved SIFT stereo matching algorithm

针对机器人视觉系统立体匹配中存在的匹配重复或错误等问题,提出了一种基于尺度不变特征变换(Scale Invariant Feature Transform,SIFT算法)和余弦相似度匹配规则的立体匹配方法。该方法以左、右两幅图像中特征向量较多的图像作为基准匹配图像,另一幅图像作为待匹配图像;再由二者的特征向量之间的余弦相似度所建立的匹配规则进行立体匹配。实验结果表明,改进型立体匹配方法有效地降低了匹配错误或重复比,具有较强的鲁棒性,匹配效果较佳,更加有利于机器人视觉系统的三维重建与定位。

Aiming at the problem of repeated or error stereo matching in the robot vision system, this paper presents a stereo matching method based on Scale Invariant Feature Transform（SIFT algorithm for short）and cosine similarity matching rules. The method regards the image which has more feature vector in the left and right images as a reference image, the other image as the matching image; then makes stereo matching with the matching rules which are designed by the cosine similarity between the feature vectors of the two matching images. Experimental results show that the improved stereo matching method can effectively reduce the matching error or repeat ratio, and has strong robustness, better matching effect,more conducive to three-dimensional reconstruction and localization of the robot vision system.