## 4 Experiment
### Experiments in Outdoor Real-World Scenarios
#### 4.5.2 Preparation Phase  
In the testing phase, the autonomous vehicle system is activated. It captures images of the test site using a stereo camera. The system processes the left-eye image to extract feature descriptors. These descriptors are then sent to the similarity computation module. In this module, the descriptors are compared to the images in the map database. The image with the highest similarity is chosen as the matching image. Upon identifying a matching image, the input and matching images are paired and fed into the feature matching network. The depth information of the matching image from the database is then compared, establishing a 2D-3D correspondence between the input and matching images. This correspondence enables the system to estimate the position and orientation of the input image in 3D space. The pose estimation algorithms are subsequently applied to optimize reprojection errors, resulting in more accurate pose estimation. To assess the effectiveness of the proposed relocalization algorithm, this study emphasizes its generalization ability in unknown environments. The algorithm's performance is evaluated across various outdoor conditions by collecting a real-world dataset. Figure 9 illustrates the data collection details and trajectory of the autonomous vehicle across two distinct outdoor scenes. The blue dots indicate the location coordinates recorded by the vehicle as it navigated the site. These points represent the vehicle's route during the map database construction phase. In the outdoor scenario, we instructed the autonomous vehicle to complete a lap around the site, capturing images at locations marked by the blue points in Figure 5. After constructing the map database, we selected eight distinct positions for testing in both Scene 1 and Scene 2. The images and corresponding locations are represented by the green points in Figure 10.  
Figure 9: Data Collection in Outdoor Environments. Figure 10 depicts the data collection process during the testing of the autonomous vehicle in two outdoor scenarios. The green points and corresponding images represent the location coordinates and image data captured by the vehicle after the map database was constructed. These data are collected to enable the relocalization function, which allows the vehicle to determine its position within the map during subsequent processes. Using the collected data, the proposed relocalization method is applied to evaluate the vehicle's relocalization performance. The predicted map coordinates for each test point are compared with the original coordinates, as shown in Figure 11. The experimental results and errors are summarized in Tables 4 and 5. Table 4 presents the localization performance of the proposed method in Scene 1. A systematic analysis of eight test data (Test1-Test8) shows that the method achieves precise vehicle localization, with the error between predicted and actual coordinates remaining low. Specifically, the average translation error in the planar coordinate system is 0.491 m. Test1 demonstrates the best performance, with an error of only 0.208 m, while Test6 exhibits a relatively larger error of 1.061 m. Nevertheless, error analysis reveals that, in most test cases, the method can maintain the error within a 0.5 m range. The experimental results  
Figure 11: Comparison of Predicted and Measured Coordinates Across Different Scenes. Figure 10: Collected Relocalization Test Coordinates and Corresponding Images. fully validate the effectiveness and reliability of the proposed method for autonomous vehicle map localization. As shown in Table 5, the proposed localization method exhibits excellent performance in the Scene 2 testing. To assess the algorithm's applicability across different scenarios, we collected eight test datasets (Test1-Test8) in Scene 2 and conducted quantitative analysis. The results demonstrate that the proposed method maintains stable localization accuracy in various environments.