Robust Loop Closure Detection based on Bag of SuperPoints and Graph Verification

Loop closure detection (LCD) is a crucial technique for robots, which can correct accumulated localization errors after long time explorations. In this paper, we propose a robust LCD algorithm based on Bag of SuperPoints and graph verification. The system first extracts interest points and feature descriptors using the SuperPoint neural network. Then a visual vocabulary is trained in an incremental and self-supervised manner considering the relations between consecutive training images. Finally, a topological graph is constructed using matched feature points to verify candidate loop closures obtained by a Bag-of-Words (BoW) framework. Comparative experiments with state-of-the-art LCD algorithms on several typical datasets have been carried out. The results demonstrate that our proposed graph verification method can significantly improve the accuracy of image matching and the overall LCD approach outperforms existing methods.