## 4 Discussion
Regarding the comparison method, [PERSON] [20] introduces a hierarchical vocabulary tree to enhance search efficiency, and in practical use, an inverted index is often used to associate each word with the images in which it appears. Typically, the number of extracted feature points ranges from 1000 to 2000, with each node in the vocabulary tree having ten child nodes and a tree depth of 6. This setup requires many computations during the word calculation phase. Despite the accelerated processing techniques used, the computational load remains significant. Experimental results show that the proposed method outperforms the comparison method regarding computational efficiency. In the third section, we evaluated the gains in the detection performance of the method. The detection performance of the proposed method is superior to SRLCD [39] and BOW2 [38]. SRLCD uses salient regions for the loop-closure judgment, but unlike semantic information, the detailed features of salient regions are not very prominent, limiting their distinctiveness. Reference [39] also mentioned adding necessary verification modules, but to make the results more intuitive during our experiments, we only compared the detection methods and disabling verification modules, which is also why the precision of SRLCD is limited. Regarding BOW2, in Reference [38], the authors constructed a hierarchical dictionary structure through clustering to avoid the enormous computational cost of directly comparing features with dictionary words one-to-one. While this provides higher search efficiency, it also reduces discrimination. Moreover, for generality, we did not conduct targeted vocabulary training for specific usage scenarios but used a general vocabulary table trained on a large number of various types of images. These two points are the main reasons affecting the detection performance of BOW2. Of course, SRLCD and BOW2 mainly focus on local information, ignoring global information, which also impacts performance. The method proposed in our work demonstrates superior detection performance compared to the comparison methods, which we attribute to the following principal reasons. Firstly, dividing the image into regions to capture more image features indeed brings better effects, consistent with the conclusions in Reference [37]. Secondly, the constructed feature descriptor uses a feature matrix that fuses image grayscale and gradient features, considering both global and detailed information in the image. The features can reflect the current usage environment without targeted training. Lastly, the multi-layer detection approach ensures detection accuracy using a more distinctive distance calculation method in the precise detection stage. In sequence 07 of Figure 10, the proposed method does not demonstrate superior performance compared to BOW2. This is related to image occlusion, as dynamic objects consistently appear in the loop-closure segment. When the proportion of dynamic objects in the images exceeds a certain threshold, the original grayscale and gradient features of the images are altered significantly, negatively impacting the detection accuracy of the proposed method. This also highlights a limitation of the proposed method, as dynamic objects can interfere with the detection precision. In the comprehensive experiments presented in the fourth section, we primarily compare localization accuracy and computational efficiency. First, by incorporating the principal direction projection error with the same number of pyramid levels, the RMSE of localization results shows an improvement of up to 23.06% compared to ORB SLAMS. Line features can effectively capture geometric information, especially in structured environments with abundant geometric edges [29]. When point features cannot effectively represent the scene characteristics, integrating line features can provide better spatial constraints. The experimental results demonstrate that point-line-based SLAM shows improved performance compared to ORB-SLAM3.