The leveraging of a VGGNet-19 and a K-means cluster in visual loop closure detection tasks

Abstract

This study is devoted to a description of a loop closure detection framework, in which the leveraging of a VGGNet-19 and a K-means cluster enables a practical, autonomous feature learning-based detecting. The principal components analysis (PCA) for dimension reduction is also investigated, guaranteeing the algorithm optimization in both accuracy and efficiency. In terms of benchmark dataset tests, the results are compared against bag-of-words (BoW) model, AlexNet and VGGNet-16, revealing our proposed design significantly outperforms others in Precision-Recall. The calculated cosine similarities and the detected closed-loop frames are simultaneously provided.