## 1 Introduction  
Simultaneous Localization and Mapping (SLAM) technology is a key technique applied to achieve self-localization and navigation in mobile robots and has been the subject of extensive research in recent years [1]. Visual SLAM, a subset of SLAM based on visual information [2], employs loop closure detection to effectively address the issue of pose drift in robot navigation [3]. Visual odometry, due to inherent accuracy biases or noise introduced by the current environment, produces data with noise, causing deviations in the pose estimation of the front-end visual odometry. Over time, these deviations accumulate, resulting in cumulative errors that can severely impact back-end optimization or mapping. Through loop closure detection, it becomes possible to determine whether the current environment is one that the robot has traversed before. This feature enables the adjustment of the robot's pose information, and, through back-end optimization, associates the current environment with previously built map information, facilitating the correction and updating of the map [4]. Loop closure detection can be conceptualized as a scene-recognition problem [5] involving the extraction of feature information from input images for similarity measurement to determine whether the current scene is similar to historical scenes. Traditional appearance-based loop closure-detection methods often utilize handcrafted features as the image's feature representation, with the bag-of-words (BoW) model [6] being the most representative. This method employs classical local feature points such as SIFT [7],SURF [8], and ORB [9] to form a set of local features for the image. The system treats the descriptors generated by processing the current image's features as a \"vocabulary\" and uses the K-means clustering method [10] to categorize similar types of vocabulary, constructing a large visual dictionary. Subsequently, each image can be represented in the form of a set of words through the appropriate retrieval tools. FAB-MAP, proposed by [PERSON] et al. [11], is a method that combines Bayesian models with the bag-of-words model, offering long-term stability in loop closure-detection modules. The dictionary constructed by FAB-MAP typically forms a tree-like structure containing independent branches composed of numerous clustering centers. When a new image is inputted, the system extracts its feature points and maps them into a combination form weighted by words. Based on this representation, each original image can be described by the vectors used for measuring similarity between images. Typically, the constructed bag-of-words (BoW) model is offline, and its main drawback lies in the fact that a well-trained visual dictionary may not adequately represent a new environment. Early researchers addressed this issue by incrementally constructing dictionaries. [PERSON] et al. [12] combined binary-based descriptors to incrementally process the BoW and employed Bayesian filtering for loop closure detection. In recent years, there has been a growing body of research focusing on incremental methods for the bag-of-words model. [PERSON] et al. [13] proposed an incremental BoW method called BoTW based on feature tracking. They utilized the KLT point tracker to construct the dictionary and incorporated a guided feature-selection technique. [PERSON], [PERSON] et al. [14] designed an incremental bag-of-words approach with gradient direction histograms. They partitioned and clustered image blocks into local regional features and represented them using gradient direction histograms. In loop closure detection, the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm was employed to dynamically generate and update vocabulary clustering features (CF) trees in real-time. This algorithm, combined with an inverted index, was used for efficient candidate selection and similarity computation.