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 selfsupervised 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 BagofWords (BoW) framework. Comparative experiments with stateoftheart 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.

Fast and Incremental Loop Closure Detection Using Proximity Graphs Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems. The frequently used bagofwords (BoW) models can achieve high precision and moderate recall. However, the requirement for lower time costs and fewer memory costs for mobile robot applications is not well satisfied. In this paper, we propose a novel loop closure detection framework titled FILDxe2x80x99 (Fast and Incremental Loop closure Detection), which focuses on an online and incremental graph vocabulary construction for fast loop closure detection. The global and local features of frames are extracted using the Convolutional Neural Networks (CNN) and SURF on the GPU, which guarantee extremely fast extraction speeds. The graph vocabulary construction is based on one type of proximity graph, named Hierarchical Navigable Small World (HNSW) graphs, which is modified to adapt to this specific application. In addition, this process is coupled with a novel strategy for realtime geometrical verification, which only keeps binary hash codes and significantly saves on memory usage. Extensive experiments on several publicly available datasets show that the proposed approach can achieve fairly good recall at 100% precision compared to other stateoftheart methods. The source code can be downloaded at https:github.comlAnshanTJUFILD for further studies.