Feature matching is a fundamental and crucial process in visual SLAM, and
precision has always been a challenging issue in feature matching. In this
paper, based on a multi-level fine matching strategy, we propose a new feature
matching method called KTGP-ORB. This method utilizes the similarity of local
appearance in the Hamming space generated by feature descriptors to establish
initial correspondences. It combines the constraint of local image motion
smoothness, uses the GMS algorithm to enhance the accuracy of initial matches,
and finally employs the PROSAC algorithm to optimize matches, achieving precise
matching based on global grayscale information in Euclidean space. Experimental
results demonstrate that the KTGP-ORB method reduces the error by an average of
29.92% compared to the ORB algorithm in complex scenes with illumination
variations and blur.

基于多层细粒度匹配策略，本文提出了一种名为 KTGP-ORB 的新型特征匹配方法，通过利用特征描述符在汉明空间中的局部外观相似性来建立初始对应关系，结合局部图像运动平滑性约束，使用 GMS 算法增强初始匹配的准确性，并最终采用 PROSAC 算法优化匹配，基于欧几里得空间中的全局灰度信息实现精确匹配。实验结果表明，在具有光照变化和模糊的复杂场景中，KTGP-ORB 方法相比 ORB 算法平均减小了 29.92% 的误差。