针对ORB算法特征匹配精度低的缺陷,结合金字塔光流特性,提出一种优化ORB特征匹配的方法。首先,采用区域分块法对待匹配图像进行处理,挑选出最佳匹配子块,缩小无效匹配区域;接着,对子块提取ORB关键字并计算匹配描述子得到粗匹配点对,采用金字塔光流法追踪ORB特征点,求解特征点的运动位移矢量,以此剔除粗匹配部分错误的匹配对;最后,采用随机采样一致算法进一步剔除冗余匹配点,获取更为精准的匹配对。实验结果表明,本文优化的ORB算法可以很好地满足实时性和精度的要求,特征匹配的平均耗时为原ORB算法的87%左右,且平均匹配率达98%以上。

Aiming at the defect of low feature matching accuracy of the ORB algorithm, combined with the optical flow characteristics of the pyramid, this paper proposes a method to optimize the ORB feature matching. First, the region matching method is used to process the matching images, the best trusted matching sub-blocks are selected, and the invalid matching area is narrowed. Then the ORB keywords are extracted from the sub-blocks and the matching descriptors are calculated to obtain the coarse matching point pairs. Pyramid optical flow method is used to track the ORB feature points, and the motion displacement vectors of the feature points are calculated to remove the incorrect matching pairs in the rough matching part. Finally, the random sample consensus algorithm is used to further remove redundant matching points to obtain a more accurate match. Experimental results show that the optimized ORB algorithm can well possess the real-time performance and accuracy. The average time for feature matching is about 87% of the original ORB algorithm, and the average matching rate is over 98%.