## 4 Results and Discussion
### Experiment of 2D-3D Registration
#### 4.1.4 MP-IDSSC Experiment  
To achieve accurate camera positioning and orientation, one must establish as many correct matching relationships as possible. To verify the effectiveness of the proposed method, we set up several experimental groups (Table 2). Each experimental group used a different combination of feature extraction and pose estimation methods. This study used the D2-Net, SuperPoint, and SIFT feature extraction methods for comparative experiments. SIFT is an artificially designed feature description and extraction method that is widely used. D2-Net can extract similar scale-invariant features from different images and has strong adaptability and robustness, and SuperPoint is a feature point detection and descriptor extraction method based on self-supervised training. To improve the feature extraction time performance and ensure the same conditions in the experiment, the number of feature points extracted was set to 1000. Then, to validate the performance of our IDSSC-MP algorithm, we selected three advanced algorithms for comparison in terms of feature point matching: SuperGlue [34], GMS, and RANSAC. Their source codes can be downloaded from the internet, and the default parameters were used in the settings. Figure 5: Perspective images and inverse depth images: (**a**) perspective image of P1; (**b**) inverse depth image of P1; (**c**) perspective image of P2; and (**d**) inverse depth image of P2. 1. Evaluation Metrics  
The experimental results are presented in Table 3. In this experiment, six quantitative evaluation metrics were adopted: number of correct matches (NCM), root mean square error (RMSE), camera position (CP), camera position error (CPE), camera orientation (CO), and camera orientation error (COE). The NCM and RMSE are the two main metrics used to evaluate registration performance. The NCM represents the number of matching point pairs used for the pose calculation; the higher the number, the better the registration performance. RMSE is a metric for evaluating the accuracy of the matching point position; the smaller the RMSE, the higher the matching accuracy. CP denotes the camera coordinates calculated from experiments in the ECEF coordinate system, and CPE is the error value of CP compared with the actual camera coordinates. CO denotes the camera orientation calculated from experiments in the yaw/pitch/roll system, and the unit is degree. COE is the orientation error value of the CO compared with the actual camera orientation. The unit is mrad, and 1 mrad is about 0.057 degrees. This means an error of about 1 meter beyond 1000 m. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Trial** & **Feature Extraction** & **Pose Estimation Method** \\ \hline Trial 1 & SuperPoint & IDSSC-MP \\ Trial 2 & D2-NET & IDSSC-MP \\ Trial 3 & SuperPoint & IDC \\ Trial 4 & SuperPoint & SuperGlue \\ Trial 5 & SuperPoint & GMS \\ Trial 6 & SIFT & Ransac \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental Groups. Figure 6: EDLines features and plumb lines: (**a**) red line segments are the EDLines features of P1; (**b**) green lines are the plumb lines of P1; (**c**) red line segments are the EDLines features of P2; (**d**) green lines are the plumb lines of P2. 2. Results Analysis  
The results show that the camera pose estimated by using the traditional point feature matching method (SuperGlue and GMS) has a larger vertical error because the Z-coordinate error is larger than the X, Y coordinate error, and the pitch angle error is larger than the yaw