# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
## 3 Research Methodology
### Solving Stage  
Figure 4 shows an analysis of the airplane model. The space straight lines \(l_{1}\) and \(l_{2}\) where the rear edges of the left and right wings of the airplane are located are not collinear. Because the wing of the aircraft is symmetrical, according to the plane's own coordinate system and the plane's top view, the straight lines \(l_{1}\) and \(l_{2}\) are in the plane coordinate system \(O_{p}-x_{p}y_{p}z_{p}\) in \(O_{p}x_{p}y_{p}\). The projected line on the plane has the same angle relative to the \(x_{p}\) axis and is axisymmetric about \(y_{p}\). Therefore, the aircraft yaw angle can be solved according to the space straight lines \(l_{1}\) and \(l_{2}\). According to the rear view and coordinate system of the aircraft, similar to the yaw angle solution, the roll angle of the aircraft can be calculated on the basis of the space straight lines \(l_{1}\) and \(l_{2}\). The projections of the space straight lines \(l_{1}\) and \(l_{2}\) on the \(O_{p}y_{p}z_{p}\) plane are challenging to distinguish and easily affected by noise; thus, the pitch angle is unstable. From our analysis of the side view of the aircraft, the intersection line of the left and right wing planes \(l_{3}\) on the \(O_{p}y_{p}z_{p}\) plane is parallel to the fuselage line. Therefore, the pitch angle of the aircraft can be solved by the angle between this intersection and the plane. In summary, the process of solving the 3D attitude angle of the aircraft is transformed into the process of solving the space straight lines \(l_{1}\) and \(l_{2}\) where the rear edges of the left and right wings of the aircraft are located, and the intersection line \(l_{3}\) of the two wing planes. The proposed method uses the disparity map \(I_{d}\) to obtain the target area of the aircraft and then count the points in this map according to the columns. The disparity map can be divided into three regions according to the number of corresponding points in the columns, namely, the left wing region \(I_{d_{d}}\), the fuselage area \(I_{d_{c}}\), and the right wing area \(I_{d_{c}}\). The proposed method extracts the edge space points of the left wing of the aircraft, that is, filter the edge point set in the left wing area. Next, the proposed method calculates the corresponding depth map \(I_{depth}\), according to the disparity map \(I_{d_{l}}\). Because the wing area is projected on the image plane according to the perspective projection, the order of the wing part in each column of the image is from far to near. The input image has a one-to-one correspondence with the disparity map; thus, the left wing edge point set \(\{p_{edge_{i}}\}\) can be filtered out according to the depth information of the corresponding points obtained by the disparity map calculation. In the process of screening the edge point set, the proposed method can judge whether the selected point is a noise point according to the depth information in the neighborhood and the ordinate position of the point in the image and filter out the relevant noise point. The proposed method projects the extracted left wing edge point set \(\{p_{edge_{l}}\}\) back to the image plane, as shown in the Figure 5a. The \"holes\" generated by the stereo matching algorithm can be clearly seen and lead to extraction The resulting edge point set is discontinuous and mixed with some non-edge points on the wing; such features can cause  
Figure 4: Schematic diagram of aircraft attitude measurement. great errors in solving the straight-line equation of the edge of the aircraft wing. Therefore, the proposed method combines the edge features of the image, correct each point in the left wing edge point set \(\{p_{edge_{t}}\}\), and then correct the noise points back to the accurate edge to obtain an accurate set of wing edge points \(\{p_{edge_{t}}\}\). The effect is shown in Figure 5b. Canny algorithm [28] is used in image edge detection.