Zhuolin Peng  
1 State Key Laboratory of Nuclear Power Safety Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Beijing 100840, China; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (J.L.) Zhouolin Peng  
1 State Key Laboratory of Nuclear Power Safety Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Beijing 100840, China; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (J.L.) School of Information and Communication Engineering University of Electronic Science and Technology of China, Chengdu 611731, China; [EMAIL_ADDRESS] (R.S. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (B.Y.) Honeywell (China) Advanced Solutions Co., Ltd., Chongqing 401121, China; [EMAIL_ADDRESS]  
[PERSON]  
2 School of Information and Communication Engineering University of Electronic Science and Technology of China, Chengdu 611731, China; [EMAIL_ADDRESS] (R.S. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (B.Y.) Hongywell (China) Advanced Solutions Co., Ltd., Chongqing 401121, China; [EMAIL_ADDRESS]  
[PERSON]  
2 School of Information and Communication Engineering University of Electronic Science and Technology of China, Chengdu 611731, China; [EMAIL_ADDRESS] (R.S. ); [EMAIL_ADDRESS] (Y.L. ); yanboyu@uestc. Feature extraction is always a prerequisite for tasks such as keyframe extraction, tracking, positioning, and mapping. It has a decisive influence on the performance of higher-level tasks. Traditional appearance-based methods often follow the visual bag-of-words (BoW) approach [2], where image descriptors are quantized into visual word vectors using clustering algorithms. However, the utilization of traditional handcrafted features discards geometric and structural information to some extent, making it difficult to cope with challenges like illumination changes and appearance variations. Fortunately, deep learning (DL) networks have recently made significant breakthroughs in computer vision. Many related studies have shown that deep features adaptively learned in a data-driven manner can provide more robust image representations under changing environmental conditions [4]. Therefore, the learning-based frameworks are intuitively applied to solve LCD problems [5]. The models based on convolutional neural networks (CNNs) are widely adopted for their outstanding performance and efficiency [6]. Although learning-based LCDs have shown promising performance in extracting robust features, few studies have focused on similarity measurement strategies.. Moreover, many algorithms ignore the internal mutual constraints between the feature extraction and similarity measurement processes, resulting in a lack of systematic optimization of networks. Therefore, this paper presents a novel end-to-end LCD method designed for complex scenes with distinct appearance variations. The approach employs an AlexNet-based feature extraction network (FEN) to capture high-level visual representations of images. In comparison to hand-crafted feature extraction methods, FEN demonstrates superior capability for perceiving complex environments by mining potential geometric and structural information within the image. Additionally, a self-attention component, Channel Weighting Module (CWM), is employed to extract higher-level features from regions of interest, effectively retaining key information for LCD. The Similarity Measurement Module based on the feature relationship of the patch-by-patch similarity matrix calculates the similarity of adaptive weighted, which improves the loop closure detection detection ability.Taking advantage of adaptive weighting, MetricNet focuses on the effective spatial patches for score estimation, achieving robustness for LCD in challenging environments with significant appearance variations. In summary, our main contributions are as follows:  
* Dynamic feature selection with self-attention: A novel learning-based LCD framework, incorporating the Channel Weighting Module guided by the insight of inverse document frequency in BoW, is proposed to distill the distinguishable spatial cues and regions of interest.