# Large-Scale Aircraft Pose Estimation System Based on Depth Cameras
## 3 Experiments and Results  
As illustrated in Figure 9, the depth camera scans the point cloud data of the model and transmits these data to the computing server. The computing server is responsible for point cloud preprocessing, point cloud registration, and point cloud preservation, and outputs the calculated results to the host computer. The host computer has a user-operated interface for the depth camera and computing server. All algorithms in the computing server were run in Visual Studio 2022 on Ubuntu 20.04. Table 2 presents the detailed hardware configuration of the computing server. Figure 8: (a) Aircraft point cloud before registration; (b) aircraft point cloud after registration. We used a Helios2 depth camera from Lucid Vision Labs. The depth camera operates on the principle of continuous wave modulation. As illustrated in Figure 10, the vertical-cavity surface-emitting laser emits modulated light, and the distance is calculated according to the phase shift between the emitted light and reflected light. Table 3 presents the detailed technical parameters of the Helios2 depth camera. The measurement scene is presented in Figure 11. The aircraft model was set up on a biaxial turntable, and the accuracy of the triaxial angular position of the biaxial turntable was less than 0.002\({}^{\circ}\). The biaxial turntable controlled the precise rotation of the aircraft model and obtained accurate attitude data. In order to verify the validity of our coarse registration method selection, a RANSAC coarse registration was performed by combining three key point algorithms with three feature description algorithms. The data in Table 4 are the average values obtained by the point cloud tests of three different aircraft models, where the score represents the overlap degree between two clouds (the closer the value is to zero, the higher the registration accuracy is). The ISS key point algorithm has obvious advantages, and the coarse registration effect is at its best when combined with SHOT feature description. \begin{table}
\begin{tabular}{c c} \hline \hline
**System Hardware** & **Specification** \\ \hline CPU & Intel Xeon Gold 5320 2.2 G \\ RAM size & 256 GB \\ RAM type & RDIMM, 3200 MT/s \\ Hard disk speed & 2500 MB/s \\ GPU & NVIDIA Ampere A30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computing server hardware configuration. Figure 10: Working principle diagram of ToF camera based on continuous wave modulation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Indicators** \\ \hline Resolution & 640 \(\times\) 480 pixels \\ Frame rate & 30 fps \\ Working distance & 0.3–8.33 m \\ Field of view & \(69^{\circ}\times 51^{\circ}\) \\ Output information & XYZ coordinates and intensity \\ Output format & ply \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Helios2 depth camera specifications. Figure 9: Hardware diagram of the measurement system. In the experiment, we used several depth cameras to scan the model aircraft. Through a hardware-triggered mode, the depth camera alternates exposure to avoid interference between depth cameras. We used a biaxial turntable to rotate the yaw angle of the aircraft from \(-40^{\circ}\) to \(40^{\circ}\). Because the angle accuracy of the biaxial turntable was higher than that of our measurement system, we took the angle data of the biaxial turntable as the real values in the experiment. The triaxial angle obtained by the measuring system was subtracted from the corresponding angle of the biaxial turntable to obtain the triaxial angle error of the measuring system. The triaxial angle errors are provided in Figure 12a-c.  
As can be seen in Figure 12, when the rotation angle of the model was large, our registration algorithm did not produce local optimal solutions. Moreover, different coaxial angles did not induce angle coupling. The standard deviations are an important basis for judging the degree of data dispersion.