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 bag-of-words (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 FILD’ (Fast and Incremental Loop closure Detection), which focuses on an on-line 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 real-time 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 state-of-the-art methods. The source code can be downloaded at https://github.com/AnshanTJU/FILD for further studies.

Authors

• Shan An
• Guangfu Che
• Fangru Zhou
• Xianglong Liu 0001
• Xin Ma
• Yu Chen

Keywords

• Vocabulary
• Visualization
• Simultaneous localization and mapping
• Costs
• Codes
• Source coding
• Graphics processing units
• Feature extraction
• Real-time systems
• Convolutional neural networks
• Fast Detection
• Loop Closure
• Fast Closure
• Loop Closure Detection
• Fast Loop
• Proximity Graph
• Convolutional Neural Network
• Mobile App
• Local Features
• Visual Detection
• Hash Function
• Memory Usage
• Mobile Robot
• Binary Code
• Image Retrieval
• Frame Features
• Speeded Up Robust Features
• Image Retrieval Task
• Convolutional Neural Network Features
• Term Frequency-inverse Document Frequency
• Image Registration
• Search Index
• Place Recognition
• Precision And Recall
• Closest Neighbors
• Convolutional Layers
• Ratio Test
• Hamming Distance