高效高精度光照自适应的ORB特征匹配算法

Efficient and high-precision illumination adaptive ORB feature matching algorithm

针对非均匀光照下ORB图像特征检测算法存在特征点过于聚集、匹配准确率不高等问题,提出了一种高效高精度光照自适应的ORB图像特征匹配算法。利用自适应阈值提取待测图像的oFAST特征点,通过优化的四叉树分解法均匀分配,进一步提高了低照度或高曝光区域特征点的数量,随后,根据汉明距离进行特征匹配,使用改进的RANSAC算法剔除误匹配,提高ORB算法中特征点的匹配准确率。实验结果表明,针对具有明显光照变化的数据集,相较于ORB、MA、Y-ORB及S-ORB算法,本文算法的平均特征分布均匀度提高13.1%,特征提取时间节省26.3%,综合评价指标提升18.5%,可高效完成复杂场景变化下的特征匹配,对目标识别和三维重建等领域具有较强的应用价值。

In order to solve the problems of the ORB image feature detection algorithm under non-uniform illumination,such as overly clustered feature points and low accuracy of feature matching,we propose an efficient and high-precision illumination adaptive ORB image feature matching algorithm.The oFAST feature points of the image to be measured are extracted using the adaptive threshold,and the number of feature points in the low illumination or high exposure area is further increased through the uniform distribution of the optimized quadtree decomposition method.Then,feature matching is performed according to Hamming distance,and the improved RANSAC algorithm is used to eliminate mis-matching,so as to improve the matching accuracy of the feature points in the ORB algorithm.The experimental results show that for data sets with obvious illumination changes,compared with ORB,MA,Y-ORB and SORB algorithms,the average feature distribution uniformity of our proposed algorithm is improved by 13.1%,the feature extraction time is saved by 26.3%,and the comprehensive evaluation index is improved by 18.5%.It can efficiently complete feature matching under complex environment changes,and has strong application value in the fields of target recognition and 3D reconstruction.