## 4 Experimental Results
### Metric Localization Results
(**a,b**) Depict the localization trajectories and the cumulative error distribution based on the data from the second loop; (**c,d**) show the localization trajectories and the cumulative error distribution based on the data from the third loop. The cumulative error distribution refers to the percentage of localization nodes within each error level relative to the total number of localization nodes. In the small's underground parking scene, our method resulted in an average error in the range of [0.355 m, 0.408 m] for each experiment, with the percentage of nodes with errors less than 0.6 m ranging from 83.84% to 91.55%. In contrast, [PERSON]'s method had an average error in the range of [1.155 m, 1.432 m] for each experiment, with the percentage of nodes with errors less than 0.6 m ranging from 9.83% to 16.22%. PAN-SLAM had an average error in the range of [0.587 m, 0.754 m] for each experiment, with the percentage of nodes with errors less than 0.6 m ranging from 41.80% to 53.43%. Based on the results of the metric localization experiments, our method achieved an average localization accuracy of approximately 30 cm on multiple test datasets in two typical underground parking scenarios. Moreover, around 90% of nodes had errors less than 0.6 m, indicating that our method offered a high localization accuracy, robustness, and strong applicability across different scenes. In contrast, [PERSON]'s method had an overall average localization accuracy of approximately 1.15 m, with the highest difference in the localization accuracy between different scenes reaching up to 0.94 m. This indicated that [PERSON]'s method had a lower localization accuracy and poor scene adaptability. Compared with [PERSON]'s method, our method significantly improved the localization accuracy. This improvement can mainly be attributed to the superior performance of our method in  
Figure 13: Comparison of metric localization results between our method and the comparison methods in the small’s underground parking scene. (**a,b**) Depict the localization trajectories and cumulative error distribution based on the data from the second loop; (**c,d**) show the localization trajectories and cumulative error distribution based on the data from the third loop. the nearest map node retrieval process. To explain further, they are more similar when the retrieved map node is closer to the localization node. Consequently, the number of matched local feature pairs increased, which aided in solving the pose relationship between them. The cumulative error distribution in Figures 12 and 13 demonstrated that our method exhibited minimal localization fluctuations, while [PERSON]'s method suffered from many localization outliers. This highlighted the superior stability of our method compared with [PERSON]'s method. The localization accuracy of PAN-SLAM was approximately 54 cm on multiple datasets in two typical underground parking scenarios. Compared with PAN-SLAM, our method improved the localization accuracy by around 24 cm, demonstrating a superior localization performance in various positioning scenarios. Although PAN-SLAM showed a good localization performance, it suffered from the phenomenon of cumulative errors, especially as the travel trajectory became longer, making this cumulative error more pronounced. In contrast, our method effectively avoided the accumulation of errors by employing a progressive map-matching strategy. This ensured not only localization accuracy but also maintained the robustness of the localization system. Considering the overall experimental results, Group 1 performed the best. This meant that the metric localization performance was optimal when both the localization and map data had sufficient lighting conditions.