# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
## 5 Physical Experiment
### Measurement Accuracy Evaluation  
This article considers different weather conditions, angles, and flying altitudes (i.e., different backgrounds) and collects eight datasets, including aircraft out of view and partial occlusion, among others. The measurement distance is approximately 14-33 m. The aircraft pose was measured on these eight datasets and compared with the real values recorded by  
Figure 10: Aircraft attitude measurement datasets. the visual simulation software. The attitude angles and distance RMS values of the eight datasets are shown in Figures 11 and 12, respectively. Figure 13 shows the error values of the three attitude angles of some measurement data. In most datasets, the RMS of the roll angle is small and stable within 0.5\({}^{\circ}\). However, in some of the datasets, the error of the roll angle is relatively large because of the influence of the background environment and other attitude angles. In this case, the RMS may reach 1.9\({}^{\circ}\). The RMS of the yaw angle is stable at approximately 1.0\({}^{\circ}\) but may occasionally be as high as 1.7\({}^{\circ}\). The error of the pitch angle is the largest among the errors of the three attitude angles. In most datasets, the RMS of the pitch angle is stable at approximately 2.0\({}^{\circ}\); in some datasets, the values obtained may rise to 2.5\({}^{\circ}\). In summary, the RMS of most of attitude angles which calculated by the proposed method is within 2.0\({}^{\circ}\) between 14 and 33 m.  
During the determination of the relative position of UAVs, distance, that is, the depth direction in the camera coordinate system, is the most important measurement. The proposed method calculates the distance between drones in each dataset. In all datasets, the RMS of the distance measurement result is 0.61 m. The data in Figure 12 show that most of the RMS values of the distance measurement results in all datasets are within 0.84 m.  
Figure 11: The attitude RMS of datasets. Figure 12: The distance RMS of datasets. Figure 13: Error of attitude angle. Only one dataset, where the true distance is approximately 25-30 m, has an RMS of 1.08 m. Figure 14 shows the RMS values of the results in this dataset. Our findings indicate that the proposed method can be used for collision avoidance measurements when the aircraft is sailing in the air. The aircraft attitude angles calculated by the proposed method are shown in Table 2. The error contrast between the yaw angle measured by the proposed method and the true value is relatively small and remains within 1.5\({}^{\circ}\). The error of the roll angle is clearly distinguished. When the actual yaw angle is small, the measurement error of the roll angle is small. When the yaw angle is large, the measurement error of the roll angle is large, but the RMS remains within 2\({}^{\circ}\). The error of the pitch angle is well within a stable range, and most of the measurement errors are within approximately 2.0\({}^{\circ}\). While some measurement values are occasionally greater than 2.5\({}^{\circ}\), which could affect the measurement accuracy to a certain extent, it will not bring devastating results and the safety of aircraft navigation can still be ensured. The method proposed in this paper is compared with conventional methods of calculating aircraft attitude, including PNP, EPNP [30], and [PERSON]'s method [31], to verify its validity and reliability. PNP is a traditional pose estimation algorithm, which estimates the pose of the target through multiple space points and their projection positions. [PERSON]'s method is based on parallel vision and uses the triangulation method for stereo matching and 3D reconstruction. Furthermore, the pose estimation algorithm of [PERSON]'s method is also widely used. So they were selected for comparison to validate our proposed method. In this experiment, eight key corners of the aircraft are artificially marked for the comparison algorithm to calculate the aircraft attitude. The speed of the measurement method in this paper is 12 fps.