## 2 Related Work  
According to the method used to describe the input images, appearance-based loop closure detection falls into two broad categories: those that extract local feature descriptors from parts of the image; and those that use the whole image to compute global descriptors [16].  
Global descriptors used in loop closure detection included color histograms [11], Gist descriptors [12] and descriptors based on principal component analysis [13]. [PERSON] et al. used descriptors containing gradient information of the entire image known as WI-SURF to perform localization [17], and [PERSON] et al. used BRIEF [9] as a holistic descriptor for a complete image [18]. [PERSON] et al. proposed an efficient keyframe-based relocalization method based on frame encoding using randomized ferns [19]. [PERSON] et al. introduced a CNN architecture that is trainable in an end-to-end manner directly for the place recognition task [20]. Global descriptor methods are generally faster to compute and more robust to local appearance changes, but they can be less tolerant to changes in the robot's pose.  
Local feature descriptor methods typically involve extracting distinctive regions or keypoints from images [21], and representing them with descriptors that encode their appearance or local geometry. Image similarity is then measured by comparing these descriptors between images. Since each image may contain a large number of local features, the BoW model [14] is often used to improve retrieval efficiency. The BoW model involves clustering local features into visual words, and uses the visual vocabulary to convert an image into a sparse numerical vector. A very successful algorithm, FAB-MAP 2.0 [2], which employs a vocabulary of up to 100,000 visual words and approximated the probabilities of visual word co-occurrences using a Chow-Liu tree. [PERSON] et al. built a vocabulary tree based on BRIEF [9] to discretize a binary descriptor space and use the tree to speed up correspondences for geometrical verification [4]. Building upon this work, [PERSON] et al. proposed a BoW place recognizer with ORB [10] features to achieve higher robustness and faster speed [22]. Most of the related work mentioned above relies on predefined vocabularies that are constructed from features extracted from a training image sequence. To address the limitations of this approach, [PERSON] et al. [23] proposed an online method for continuously updating the vocabulary based on observations, while still maintaining the ability to match prior observations with future ones. Additionally, [PERSON] et al. [5] introduced an incremental BoW approach that builds a vocabulary consisting of binary visual words in an online, incremental manner by tracking features between consecutive frames. Building on this algorithm [5], [PERSON] et al. [6] used a hierarchical and incremental structure to reduce the complexity of the BoW assignment process.  
To incorporate the advantages of both local and global descriptors, several loop closure detection methods using both techniques can be found in [24; 25]. [PERSON] et al. [24] computed the global descriptor HOG [26] on image patches to learn distinctive visual elements called scene signatures. [PERSON] et al. [25] used a convolutional network (ConvNet) to extract features for each region landmarks.