## 5 Conclusions  
In this work, we proposed an appearance-based loop closure detection algorithm that uses an incremental Bag of Words scheme with gradient orientation histograms to retrieve similar images. The proposed local region feature extraction method extracts local region features by dividing image blocks and clustering them based on gradient orientation histograms. Our online, incremental BoW model is based on the BIRCH algorithm to generate and update a vocabulary CF tree that can maintain a small vocabulary size under large image datasets. The CF attribute of the vocabulary CF tree can estimate the frequency of word occurrence in real-time, enabling the calculation of image similarity. Finally, islands are generated by grouping highly similar images that are close in time, and loop closure candidates are obtained through competition between islands. We evaluated the proposed approach on publicly available datasets and demonstrated its ability to generate high recall and precision. A loop closure detection algorithm with high accuracy and recall plays a crucial role in achieving reliable place recognition during the operation of a SLAM system. By incorporating loop closure constraints, the cumulative error in the estimated trajectory can be effectively corrected, thus significantly enhancing trajectory estimation accuracy and overall mapping quality.  
While our proposed approach demonstrates excellent accuracy and efficiency, there are still areas that warrant further improvement. For instance, we recognize the need to refine the shape restriction of extracted features by grid-divided image blocks and enhance the quality of the vocabulary tree constructed using the BIRCH algorithm. Regarding  
Figure 9: The response time of our proposed approach for these four sequences.  
future work, we intend to focus on local region feature extraction and vocabulary tree construction. We will investigate other local region feature extraction methods, including methods for image region selection and feature description. In addition, we plan to optimize the construction and updating processes of the vocabulary tree by implementing staged optimization and pruning approaches, which can optimize the structure of the vocabulary tree. Furthermore, our plan involves integrating our loop closure detection method into a comprehensive SLAM framework. By integrating our method into the complete SLAM system, we anticipate a substantial improvement in the overall performance and accuracy of the system.  
Conceptualization, Y.L. and W.W.; methodology, Y.L. and H.Z.; software, Y.L.; validation, Y.L., W.W. and H.Z.; formal analysis, Y.L. and W.W.; investigation, Y.L., W.W. and H.Z.; resources, Y.L. and W.W.; data curation, Y.L. and H.Z.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L.; visualization, Y.L.; supervision, H.Z.; project administration, W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 61573148; 2020 Guangdong Higher Vocational Colleges Industry and education integration innovation platform project, project number 2020 JCJPT026.  
Not applicable.  
The authors declare no conflict of interest.