Hierarchical Clustering Matching for Features with Repetitive Patterns in Visual Odometry

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.