## 4 Experimental Results
### Nearest Map Node Search Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(K\)’s Value} & \multicolumn{3}{c}{**Industrial Park**} & \multicolumn{2}{c}{**Mall**} \\ \cline{2-6}  & **2 nd Loop** & **3 rd Loop** & **2 nd Loop** & **3 rd Loop** \\ \hline \multirow{4}{*}{Accuracy} & \(K=2\) & 93.01 & 92.33 & 87.53 & 85.46 \\  & \(K=3\) & 93.85 & 93.03 & 90.78 & 89.69 \\  & \(K=4\) & 95.25 & 94.08 & 93.49 & 91.86 \\  & \(K=5\) & 95.81 & 94.77 & 95.36 & 93.67 \\  & \(K=6\) & 96.37 & 93.73 & 94.11 & 92.16 \\  & \(K=7\) & 95.81 & 93.03 & 93.00 & 91.13 \\  & \(K=8\) & 94.97 & 92.33 & 92.33 & 90.59 \\ \hline \multirow{4}{*}{Average error (frames)} & \(K=2\) & 0.081 & 0.098 & 0.173 & 0.188 \\  & \(K=3\) & 0.078 & 0.087 & 0.120 & 0.128 \\ \cline{1-1}  & \(K=4\) & 0.053 & 0.066 & 0.080 & 0.093 \\ \cline{1-1}  & \(K=5\) & 0.045 & 0.059 & 0.053 & 0.071 \\ \cline{1-1  
Based on the experimental results of the nearest map node retrieval, it was evident that the proposed method achieved a retrieval accuracy of over 92% in all scenes, with an average error not exceeding 0.09 frames. This reflects the high precision, robustness, and strong applicability of our method across different scenes. In contrast, [PERSON]'s method failed to achieve a retrieval accuracy exceeding 78% in all scenes, and the discrepancy in retrieval accuracy between the different scenes could be as high as 33%. This indicates that [PERSON]'s method has a lower retrieval accuracy and poorer scene adaptability. Compared with [PERSON]'s method, our method significantly improved accuracy. This improvement can be attributed to our method's encoding of images into a holistic feature and using the distance between the holistic features to represent the similarity between the nodes, which provided a more comprehensive and rational representation. Moreover, our method successfully resolved the problem of ambiguous recognition by utilizing the WH-KNN algorithm, which significantly enhanced the accuracy. [PERSON]'s method relies on the quantity of matched local feature pairs to measure the node similarity. However, erroneous matching pairs can have a detrimental impact on the representation of node similarity, leading to a reduced retrieval accuracy. Our method maintained a difference in accuracy of less than 2% under different lighting conditions for the same source data, while the accuracy difference for [PERSON]'s method reached up to 22%. This demonstrated that our method's resistance to lighting interference far surpassed the comparison method's. The sensitivity of [PERSON]'s method to lighting conditions was because when the lighting is sufficient, there was an abundance of matched local feature pairs, and the proportion of erroneous matching pairs among them was relatively low, resulting in a minor impact on the retrieval accuracy. However, when the lighting was weak, there were fewer matched local feature pairs, and the proportion of erroneous matching pairs among them was higher, significantly affecting the retrieval accuracy. In contrast, our method represented the similarity between the nodes based on the distance between the holistic features, effectively avoiding these issues and enhancing the resistance to lighting interference.