## 1 Introduction
However, most loop closure-detection methods based on handcrafted features are primarily effective in static environments and struggle to handle drastic changes in viewpoints and variations in lighting conditions, making them less accurate in accurately identifying loops in complex dynamic environments. In recent years, the rapid development of deep learning has introduced new approaches for loop closure detection. [PERSON] et al. [15] utilized autoencoders to extract deep features from images and employed a similarity matrix for loop closure-detection decision-making. Building on prior work, [PERSON] et al. [16] used the deep learning network PCANet to extract features from images, demonstrating that features extracted by this network outperform manually designed features. [PERSON] et al. [17] designed a lightweight autoencoder feature extractor and introduced a local matching algorithm based on motion-vector consistency constraints, ensuring high recall rates while enhancing real-time performance. [PERSON] et al. [18] combined residual networks and capsule networks to simultaneously extract shallow geometric features and deep semantic features from images, reducing noise in images and accelerating model convergence. Among various deep learning methods, convolutional neural networks (CNNs) possess the capability to learn hierarchical features directly from raw data. The invariant features output by the convolutional layers enable them to accomplish highly discriminative and complex visual tasks [19]. CNNs exhibit invariance to rotation, translation, and lighting conditions, making them well suited to optimizing loop closure detection in visual SLAM, such as in image retrieval and classification tasks. Unlike handcrafted features based on human intuition, CNNs can be trained using datasets like ImageNet [20], Places-365 [21] and MS-COCO [22], allowing the network to automatically learn representations from a large amount of training data. This adaptability to different environmental conditions is a notable advantage. [PERSON] et al. [23] trained an image feature extractor on the Places dataset using the Caffe framework and observed that, when lighting conditions changed, the performance of the convolutional network was significantly superior to that of manually designed image descriptors. [PERSON] et al. [24] compared image features extracted by CNN with traditional descriptors and found that, in high-light environments, CNN features exhibited superiority, with a noticeable speed advantage on the CPU compared to handcrafted features. [PERSON] et al. [25] conducted research revealing that the top-layer encoding of CNNs possesses a certain level of robustness to changes in perspective, addressing the issue of traditional bag-of-words models being unsuitable for adaptation to drastic viewpoint changes. However, the performance of convolutional neural networks (CNNs) continues to face new challenges in complex outdoor environments, particularly in the presence of moving objects and obstacles. In such environments, the image features extracted by the backbone network often contain significant redundancy. Addressing this issue, [PERSON] et al. [26] modified the convolutional network and proposed a Hybrid Deep Learning Architecture (HDLA) specifically designed to generate high-level semantic image features for loop closure detection. This learning framework can generate advanced semantic features for images, enabling accurate loop detection even in complex environments. [PERSON], [PERSON] [27], discovered a strong correlation between the image features extracted by CNN and the bag-of-words model. They combined the global features of CNN with image descriptors generated based on the bag-of-words model, establishing a loop closure-detection system with spatial awareness and semantic information.