# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
###### Abstract
The pyramid-based regional matching algorithm may be used to determine the initial value of the target's pose parameters, after which the iterative optimization algorithm is refined and optimized on the basis of outer contour point set matching. [PERSON] et al. [16] proposed a combined \begin{table}
\begin{tabular}{c c c} \hline \hline contact & GNSS & In ref. [5; 6], GNSS is used to solve the aircraft pose, but the data update frequency of GNSS is slow, and the signal transmission is easily affected. \\ (attitude internal measurement) & IMU & In ref. [7; 8], IMU is used to calculate aircraft pose, but the positioning error of IMU increases with time, and the long-term accuracy is poor. \\ \hline \multirow{4}{*}{non-contact measurement} & \multirow{4}{*}{Monocular measurement} & Ref. [9] proposed SoftPOSIT, which can solve pose parameters iteratively. \\  & & Ref. [10] proposed the N-point perspective problem, which is a PNP problem. \\  & & Ref. [11] proposed a method to generate a template to estimate the target pose. \\  & & Ref. [12] performed pose measurement based on contour matching method. \\  & & Ref. [13] recognized the lines of aircraft to realize the structure extraction. \\ \cline{2-3}  & \multirow{4}{*}{Binocular measurement} & Ref. [14] proposed a method to solve aircraft pose by using the line feature. \\  & & Ref. [15] proposed a method to compare simulated images with real model. \\ \cline{1-1}  & & Ref. [16] proposed a combined vision technology based on a multi-camera. \\ \cline{1-1}  & & Ref. [17; 18] proposed optimization-based methods to estimate aircraft pose. \\ \cline{1-1}  & & Ref. [19] extracted and clustered image line features to solve aircraft pose. \\ \cline{1-1}  & & **Proposed Method** proposes pose measurement based on rigid skeleton. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of pose measurement methods. vision technology based on a multi-camera, which combines multi-view and mono-view constraints for UAV attitude estimation. The particle filter framework combined with constraints to improve the accuracy of the algorithm. [PERSON] et al. [17; 18] combined stereo vision with the geometric information of the object, proposed a method based on optimization to measure the position and attitude of the spacecraft and realized the attitude solution of the spacecraft with the non-cooperative target. [PERSON] et al. [19] extracted and clustered image line features based on two widely separated cameras, combined with the common geometric constraints of straight wing aircraft to solve the aircraft attitude. The aircraft attitude measurement is realized under the condition of the large field of view and long-distance. In practical applications, an image may be unavailable or the target may not be captured when a photometric device is used to capture images. Furthermore, algorithms that rely on 2D images usually need other information or equipment to solve the spatial pose accurately. Existing multi-eye measurement methods mainly match images with models or seek the correspondence between 2D and 3D points. These methods are negatively affected by factors such as inaccurate contour extraction, occlusion of key feature points, and incorrect matching in complex environments. Therefore, the pose measurement accuracy of these methods is low, and knowledge of the aircraft model information must be available. In the process of flight, the skeleton structure of UAVs is rigid and almost not affected by external factors. Therefore, the attitude of UAV can be solved by the spatial straight line in the rigid skeleton. This paper proposes a measurement method for the position and attitude of UAV based on a rigid skeleton that integrates 2D images and 3D point cloud information without requiring aircraft size or model information and then solves the problem of UAV aircraft attitude determination.