## 1 Introduction
These descriptors can be classified into local and global feature descriptors [36]. Local feature descriptors, such as SIFT, effectively represent image details and are robust to changes in viewpoint and scale. Global descriptors, such as Histograms of Oriented Gradients (HOGs) [37], primarily capture global image information and are highly adaptable to lighting conditions. The Bag-of-Words (BOW) model, which represents feature descriptors using visual words, is one of the effective methods for image feature representation [38]. Loop-closure detection based on local features generally combines BOW. After calculating the corresponding word for the local features, the word frequency is counted and weighted, followed by similarity computation. BOW-based loop-closure detection requires offline vocabulary training, making it inconvenient, and its performance is heavily influenced by the training data. If the training and application data differ significantly, detection accuracy decreases. Furthermore, dictionaries typically have large sizes to enhance adaptability to different scenes, which poses efficiency challenges. Unlike BOW, [PERSON] et al. [39] proposed a novel approach for loop-closure detection based on salient regions, SRLCD, matching directly in the frequency domain. Salient regions can also be considered a type of local feature. Among global descriptors, the Gradient Orientation Histogram [37] and Grayscale Histogram [40] are widely used. [PERSON] et al. [37] employed the Gradient Histogram as an image feature, combined with SVM, achieving significant success in human detection, demonstrating the potential of global features in image detection. [PERSON] et al. [36] also used the Gradient Orientation Histogram as a descriptor for image representation and introduced image block division and clustering to enhance the local description. The proposed method also incorporated an online incremental vocabulary method based on BOW, offering better environmental adaptability. However, until the vocabulary accumulates to a certain extent, the method cannot detect accurately, and the complexity of computing feature descriptors introduces additional time consumption and performance uncertainty. [PERSON] et al. [41] di rectly employed a combination of the Grayscale Histogram and key region covariance for loop-closure detection, achieving higher computational efficiency. Moreover, neural networks can directly extract image features, replacing hand-engineered features in loop-closure detection. Neural-network-based loop-closure detection methods have garnered significant attention. [PERSON] et al. [42] used Convolutional Neural Networks (CNNs) to extract image features, combined with spatial and sequential detection for loop-closure detection, achieving good adaptability to viewpoint changes and high detection accuracy. [PERSON] et al. [43] selected multiple image blocks in the image using key points, such as SIFT, and employed an unsupervised deep learning network, a stacked denoising auto-encoder (SDA), to extract feature representations for similarity computation to achieve loop-closure detection. [PERSON] et al. [44] incorporated deep neural networks (DNNs) for feature extraction and matching in the loop-closure-detection stage, which improved the accuracy of loop-closure detection. In Reference [45], the authors considered the low-latency characteristics of Spiking Neural Networks (SNNs) and proposed a lightweight and fast place-recognition method called VPRTempo. Neural networks can also extract high-level image features, such as semantic features, which can be applied in loop-closure detection. [PERSON] et al. [46] replaced traditional image features with semantic vectors and calculated vector similarity to filter candidate keyframes. [PERSON] et al. [47] computed the similarities of GIST features, semantic features, and appearance features separately and then performed loop-closure detection using a weighted fusion approach. High-level semantic features and hand-engineered features provide complementary information.