## 4 Conclusions  
This paper proposes a loop detection method based on feature compression and convolutional neural networks (CNNs). In contrast to traditional image similarity-detection methods that directly use the global features output by CNNs, the method proposed in this paper compresses and post-processes the network's output to eliminate redundant information from the features, addressing the impact of moving objects, occlusions, and shadows on image similarity measurement in dynamic environments. Specifically, in the compression operation, a compression ratio was introduced. This feature allows researchers to choose an appropriate compression ratio based on the complexity of the environment. Subsequently, the compressed features are processed through NetVLAD to generate more powerful image descriptors for image similarity detection. Experiments were conducted using two publicly available datasets to evaluate the performance of the proposed method, with evaluation metrics including PR curves and the maximum recall rate at 100% precision. In the first stage of the experiment, we tested various convolutional layers of ResNet152, observing the performance of conv1-conv5 at different compression ratios. It was found that the conv5 layer performs well on scenes with dynamic objects when the compression ratio is set to 80% or 90%. In the second stage of the experiment, we compared our method with feature-based loop detection methods and other deep learning-based loop detection methods. The experimental results show that, with the New College dataset, the proposed improved method achieved an average accuracy improvement of approximately 9.98% compared to the original network, and with the City Center dataset, there was an improvement of about 2.64%. In terms of real-time performance, there was an improvement of approximately 23.51% compared to the original network.  
However, in different outdoor environments, the implementation of this method relies on manually selecting the compression ratio. While the network can adapt to most environments with a compression ratio of 70% or higher, when there are significant changes in the background, it is necessary to manually reduce the compression ratio to reduce the impact of changes in the background shadows on feature representation. However, a too-low compression ratio makes it challenging to resist the interference of dynamic objects and weakens the dimensionality-reduction capability of the convolutional layer output features. This tradeoff means that the proposed method is suitable only for environments that are complex but have only small changes in the background overall. Further optimization is needed in the future.  
**Author Contributions:** Conceptualization, S.J.; methodology, S.J. and Z.Z.; software, Z.Z. and S.S.; validation, S.J., Z.Z. and S.S.; formal analysis, S.J.; investigation, S.J.; resources, S.J.; data curation, S.J.; writing--original draft preparation, S.J. and Z.Z.; writing--review and editing, S.J. and Z.Z.; visualization, Z.Z. and S.S.; supervision, S.J.; project administration, S.J.; funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Special Research Fund for Non-profit Sector (No. 201404402-03).  
Not applicable to research that does not involve humans or animals.  
Not applicable.  
The datasets generated and/or analyzed during the current study are openly available in the Dynamic Environment Stereo Test which is a public dataset which is available from a given URL for everyone according to their needs [[https://www.robots.ox.ac.uk/~mobile/IJRR_2008_Dataset/](https://www.robots.ox.ac.uk/~mobile/IJRR_2008_Dataset/)]([https://www.robots.ox.ac.uk/~mobile/IJRR_2008_Dataset/](https://www.robots.ox.ac.uk/~mobile/IJRR_2008_Dataset/)).  
The authors declare no conflict of interest.