In this study, a hierarchical clustering matching (HCM) algorithm is proposed to match features with ambiguity due to repetitive patterns in visual odometry. Visual odometry is a real-time system that estimates the motions of camera setups, in which feature matching is a key step for tracking and relocalization. However, it is still difficult to remove outliers fast and reliably when a high proportion of outliers exist. The proposed HCM algorithm solves this problem by clustering accurate matches hierarchically. Dubious matches excluded from any clusters are removed during the iterations, which finally converges when new clusters no longer generate. Local geometric consistency and descriptor of features are both considered to be a metric to link two clusters using a centroid linkage criterion. Experimental results demonstrate that the proposed method works well on solving the problem mentioned above. Compared to state-of-the-art methods on feature matching, HCM performs much better on efficiency with comparable accuracy.

在这项研究中，由于视觉测距法中的重复模式，提出了一种层次聚类匹配（HCM）算法来匹配具有歧义的特征。视觉测距法是一种实时系统，可估计摄像机设置的运动，其中特征匹配是跟踪和重新定位的关键步骤。但是，当存在大量异常值时，仍然难以快速，可靠地删除异常值。提出的HCM算法通过对准确的匹配进行分层聚类来解决此问题。从任何群集中排除的可疑匹配项会在迭代过程中被删除，当不再生成新群集时，这些匹配最终会收敛。局部几何一致性和特征描述符都被认为是使用质心链接准则链接两个聚类的度量。实验结果表明，该方法可以很好地解决上述问题。与最新的特征匹配方法相比，HCM的效率要高得多，且精度可比。