  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.

Robotics;Computer Vision and Pattern Recognition