## 1 Introduction
After training, this network can perform holistic feature extraction on input image sequences, generating advanced image feature vectors for image-similarity detection. Considering that the primary objective of loop closure detection is to identify the location of the images, the Places-365 dataset was selected for feature-extraction training during the process of network training. 2. **Incorporation of KL Transformation for Feature Compression:** To mitigate the impact of dynamic environments on image-similarity detection, reduce the probability of false detections, and preserve important information within features to the maximum extent, this paper introduced KL transformation as a standalone feature-compression module. Inserted at the output end of the Siamese neural network, it compresses and reduces the dimensionality of the high-level feature information outputted by the network. The KL transformation aims to eliminate redundancy in information and adjusts the compression ratio based on different scenes and object configurations in the images. 3. **Generation of Spatially Informed Image Descriptors using NetVLAD:** The compressed features were inputted into NetVLAD to generate image descriptors with spatial information for similarity detection. The proposed method was tested using publicly available datasets. To evaluate model performance, different backbone networks for feature extraction and various compression methods were tested in loop closure detection. Results indicate that the compressed features exhibit enhanced representational capabilities for dynamic scenes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Method** & **Advantages** & **Shortcomings** \\ \hline \multirow{3}{*}{**Traditional methods**} & **BoW** & 1. Simple and easy to execute & 1. Time-consuming 2. Inability to adapt to a new environment \\ \cline{2-4}  & **Incremental BoW** & 1. Improved generalization ability of the model & 1. Time-consuming 2. Inability to adapt to a new environment \\ \hline \multirow{3}{*}{**Deep Learning methods**} & **CNN** & 1. Powerful image feature extraction capabilities & 1. computationally intensive and occupies memory 2. Strong generalization ability & 1. Computationally intensive and occupies memory 2. susceptible to interference from dynamic objects \\ \cline{2-4}  & \multirow{3}{*}{**CNN-AE**} & 1. Data dimensionality reduction & 1. Loss of information 2. Unsupervised, strong generalization ability & 2. Overfitting \\ \cline{2-4}  & \multirow{3}{*}{**CNN-PCA**} & 1. Data dimensionality reduction & 1. Loss of information 2. Removal of redundant information & 2. Difficult of setting parameters \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of methods for loop detection. This paper is divided into four main sections. The Section 1 is the introduction, providing an overview of the trends in related or similar research and highlighting the innovative aspects of the proposed method. The Section 2 covers the theoretical derivation and the detailed implementation of the proposed method. The Section 3 presents the experimental analysis, including details of the experimental platform and datasets used, as well as an analysis of performance metrics for the network model. Finally, the Section 4 comprises the conclusion.