A DenseNet featurebased loop closure method for visual SLAM system * Loop closure is a crucial part in SLAM, especially for large and longterm scenes. Utilizing offtheshelf networksxe2x80x99 features in loop closure becomes a hot spot. However, what kind of network is more suitable in loop closure and how to use their features have not been wellstudied. In this paper, DenseNet is introduced in this field according to its own characters. The features of DenseNet preserve both semantic information and structure details and outweigh other popular networksxe2x80x99 features significantly. Based on this, a DenseNet featurebased framework, named DenseLoop, is proposed to address the loop closure problem. Weighted Vector of Locally Aggregated Descriptor (WVLAD) method is used to encode the local descriptors as the final global descriptor, which could resist geometry structure and viewpoint changes. Furthermore, 4 maxpooling by channel and localitysensitive hashing (LSH) are adopted to accelerate the search process. Extensive experiments are conducted on public datasets and the results demonstrate DenseLoop could achieve stateoftheart performance.

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.