# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 4 Experiment and Analysis
### Rotation Experiment at High Speed  
The kinetic data are consistent with those in Section Rotation Experiment at Low Speed. The experimental conditions were set at a relatively high-speed rotation (approximately 12 degrees per frame). Figure 17a,b show the X-, Y-, and Z-axis position and Euler angle curve results during the high-speed rotation process. Figure 17c,d illustrate the X-, Y-, and Z-axis position and Euler angle error results during the rotation process. The method proposed in this paper can cope with the high-speed rotation condition from the data in the figure above. The target could be tracked stably in the high-speed experiment. The maximum error and average error of the X, Y, and Z three-axis position of  
Figure 17: Pose estimation experimental results at high speed: (**a**) absolute position; (**b**) absolute angle; (**c**) error of position; (**d**) error of angle. the proposed method were 14.4, 12.3, and 5.9 mm and 4.9, 3.5, and 1.08 mm, respectively. The maximum error and average error of the X, Y, and Z three-axis angle of the proposed method were 0.82\({}^{\circ}\), 0.60\({}^{\circ}\), and 1.36\({}^{\circ}\) and 0.29\({}^{\circ}\), 0.26\({}^{\circ}\), and 0.39\({}^{\circ}\), respectively. Contrasted with the working conditions in Section Rotation Experiment at Low Speed, the position and angle errors increased, which still indicates that the task of capturing non-cooperative spacecrafts can be completed. Figure 18 represents the X, Y, and Z three-axis position and Euler angle error results obtained by the proposed method, the stereo vision algorithm, and the traditional FPFH + ICP algorithm in the high-speed process. Figure 18: Comparison of the error results of the different methods at high speed: (**a**) X-axis position error; (**b**) Y-axis position error; (**c**) Z-axis position error; (**d**) X-axis angle error; (**e**) Y-axis angle error; (**f**) Z-axis angle error. The maximum error and average error of the X, Y, and Z three-axis position of the stereo vision method were 28.7, 30.4, 13.4 mm and 10.4, 10.4, and 3.2 mm, respectively. The maximum error and average error of the X, Y, and Z three-axis angle of the stereo vision method were 5.9\({}^{\circ}\), 3.0\({}^{\circ}\), and 4.6\({}^{\circ}\) and 0.96\({}^{\circ}\), 1.01\({}^{\circ}\), and 1.3\({}^{\circ}\), respectively. The error trend of the FPFH method in the high-speed rotation was coincident with the proposed method. The maximum error and average error of the X, Y, and Z three-axis position of the FPFH method were 21.3, 18.5, and 9.41 mm and 7.4, 4.8, and 3.7 mm, respectively. The maximum error and average error of the X, Y, and Z three-axis angle of the FPFH method were 1.6\({}^{\circ}\), 1.21\({}^{\circ}\), and 2.36\({}^{\circ}\) and 0.39\({}^{\circ}\), 0.34\({}^{\circ}\), and 0.77\({}^{\circ}\), respectively. Key Frames Threshold Selection Experiment  
The selection of the key frames threshold had a great influence on eliminating the cumulative error in the process of pose measurement. Figure 19 shows the pose errors resulting from the different thresholds. As can be seen from the figure, the selection of the key frames threshold in this manuscript was appropriate. Small thresholds will lead to more memory consumption and increase the amount of computation in the pose measurement system. The suitable thresholds \(T_{d}\) and \(T_{a}\) can prevent the error drift phenomenon in the rotation process. The threshold \(T_{c}\) updates the set of key frames when the relative position and angle do not meet the conditions for a long time. From the perspective of memory and computation, more ground experiments are required to select the optimal threshold values in practical application. Calculation Time Comparison of Initial Value Acquisition Methods  
For a point cloud with N points and K-neighbors for each point, the computational complexity of the FPFH method is O(NK). However, the stereo vision method is O(M). M is the number of feature points.