## 1 Introduction
While the aforementioned methods partially address issues arising from environmental changes, deep convolutional neural networks, due to the stacking of intermediate layers, often involve a large number of parameters, making the process of extracting image features time-consuming and resource-intensive in terms of memory usage. Therefore, some researchers have shifted their focus to studies on post-processing after dimensionality reduction in convolutional neural networks. [PERSON], [PERSON] et al. [28] combined stacked autoencoder networks for data dimensionality reduction with convolutional neural networks for classification. They leveraged the advantages of multidimensional remote-sensing information to alleviate the impact of dimensionality on classification accuracy, enhancing the effectiveness of crop classification. [PERSON], [PERSON] et al. [29] integrated a pre-trained CNN model with an autoencoder, proposing CNN-AE to obtain low-dimensional feature vectors as image representations. They decomposed and reconstructed the similarity matrix to address the issue of perceptual aliasing in loop closure detection. [PERSON], [PERSON] et al. [30] combined Principal Component Analysis (PCA) with convolutional neural networks to reduce the dimensionality of hyperspectral images, eliminating non-linear consistency features between wavelengths and improving the accuracy of visualization and classification. However, autoencoders and PCA have limitations in terms of understanding the data, and when dealing with images in complex environments, they may lead to the loss of important data. To gain a better understanding of the strengths and weaknesses of various loop closure-detection methods, this work summarizes and consolidates the methods discussed above, along with their respective advantages and disadvantages (Table 1). Through Table 1, it can be observed that the traditional bag-of-words model has lost its advantage in scene generalization compared to deep learning methods. However, deep learning, specifically CNNs, sacrifices memory and computation time in exchange for efficient image feature representation. In addressing this challenge, the research referenced in this paper explores Principal Component Analysis (PCA), which can reduce the dimensionality of the feature vectors output by CNN. Nevertheless, in the compression process, if only a small portion of the principal components is retained, the information loss may impact the model's understanding of the input data. Therefore, this paper proposes a loop closure-detection method suitable for dynamic environments. The approach employs KL transformation to compress the features extracted by CNN, addressing the impact of factors such as moving objects and environmental occlusions on the accuracy of loop closure detection in dynamic environments. In comparison to direct PCA-based dimensionality reduction, KL transformation achieves dimensionality reduction by maximizing information content, placing greater emphasis on the interdependence of information within the data. This approach helps preserve key features of the data more effectively. Additionally, KL transformation is a non-linear change, offering greater flexibility in capturing non-linear relationships within the data compared to the linear transformation of PCA. KL-transformed feature matrices from the CNN output serve as input to NetVLAD, constructing image-feature descriptors with spatial-representation capabilities for similarity detection. The overall loop closure-detection process can be divided into three steps: backbone network feature extraction, feature compression, and image-sequence similarity detection. In summary, the research conducted in this paper includes the following:  
1. **Utilization of Pre-trained ResNet152 as Backbone Feature Extractor:** This work employed a pre-trained ResNet152 as the backbone feature-extraction sub-network and designs a Siamese neural network with shared weights.