## 3 Results
### Experimental Comparison
Figure 20: Precision–recall curves of different convolutional layers in ResNet152 under various compression ratios in the New College dataset (**a–d**): Results at compression rates of 60%, 70%, 80%, and 90%. Finally, we compared our proposed method with other commonly used loop closure-detection methods on the New College and City Center datasets, including:  
1. **Traditional loop closure detection methods based on manually designed feature points:** SeqSLAM [38], FAB-MAP [39]. 2. **Deep learning-based loop detection methods:** NetVLAD based on ResNet152, VGG [40], and Faster RCNN [41]. The experiment does not consider the output of the network's lower layers, but selects the conv5 layer of ResNet152 with a compression ratio \(\gamma\) set to 80%. Table 6 in the experimental results presents the maximum recall rate for each method at 100% precision. Figure 22 illustrates the PR curves for each method on the New College dataset, and Figure 23 illustrates the PR curves for each method on the City Center dataset. The experiments primarily focused on comparing deep learning methods. From Table 6, it can be observed that among the three NetVLAD-based methods, the one with VGG as the backbone network exhibits a relatively lower maximum recall at 100% precision. Following that in maximum recall is the original ResNet152, and the post-processed ResNet152 achieves a maximum recall of 60.14% in the New College dataset and 73.38% in the City Center dataset. Particularly, Figure 22 indicates that deep learning-based methods significantly outperform traditional feature-point-based methods. As ResNet152 itself is a deep neural network, it exhibits notably strong performance on both datasets. Moreover, the compressed features can resist interference from moving objects. Coupled with the spatial representation capability of NetVLAD's output feature descriptors, our proposed method further enhances the performance of the NetVLAD based on the ResNet152 backbone. In visual SLAM systems, there is a high demand for real-time performance in the loop-closure detection module to achieve synchronous localization and mapping. With this consideration in mind, we randomly selected 2000 images from the New College and City Center datasets and compared the average time required to construct single-image feature descriptors between traditional manual methods and deep learning methods. Table 7  
Figure 21: Precision-recall curves of different convolutional layers in ResNet152 under various compression ratios in the City Center dataset (**a–d**): Results at compression rates of 60%, 70%, 80%, and 90%. From the statistical results, it is evident that traditional methods, such as SeqSLAM and FAB-MAP, exhibit longer feature-extraction times for a single image compared to the other four deep learning-based methods. This result suggests that the efficiency of extracting features from deep learning outputs surpasses the efficiency of constructing descriptors by querying dictionaries. These four deep learning methods can be divided into those without the VLAD module and those with the VLAD module. The former, represented in this study by Faster RCNN, is the fastest network, with a feature-extraction time of 11.62 ms per single image, but it does not demonstrate significant advantages in the accuracy comparison presented earlier. The latter group includes VGG_NetVLAD, ResNet152_NetVLAD, and ResNet152_KL_NetVLAD. VGG, being an early CNN network with a long intermediate layer structure, introduces increased complexity due to its continuously stacked convolutional layers, resulting in lower efficiency compared to the other three methods. In contrast, ResNet152, which incorporates residual modules, significantly reduces the network's consumption of computational resources, shortening the feature-extraction time by approximately 27.88% compared to VGG. The method proposed in this paper, which utilizes KL transformation for post-processing, reduces the dimensionality of the feature vectors from each convolutional layer output of ResNet152.