# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
###### Abstract  
Pose measurement is a necessary technology for UAV navigation. Accurate pose measurement is the most important guarantee for a UAV stable flight. UAV pose measurement methods mostly use image matching with aircraft models or 2D points corresponding with 3D points. These methods will lead to pose measurement errors due to inaccurate contour and key feature point extraction. In order to solve these problems, a pose measurement method based on the structural characteristics of aircraft rigid skeleton is proposed in this paper. The depth information is introduced to guide and label the 2D feature points to eliminate the feature mismatch and segment the region. The space points obtained from the marked feature points fit the space linear equation of the rigid skeleton, and the UAV attitude is calculated by combining with the geometric model. This method does not need cooperative identification of the aircraft model, and can stably measure the position and attitude of short-range UAV in various environments. The effectiveness and reliability of the proposed method are verified by experiments on a visual simulation platform. The method proposed can prevent aircraft collision and ensure the safety of UAV navigation  
the attitude measurement method is affected by factors such as calculation frequency and signal reception. The attitude external measurement of UAV algorithms has been studied by research institutes worldwide. Some methods measure the aircraft's pose by monocular. These methods usually need measuring equipment such as theodolite and radar. [PERSON] and [PERSON] [9] proposed SoftPOSIT, an improved algorithm of POSIT. In this algorithm, the target pose parameters can be solved iteratively when the corresponding 2D-3D points are unknown. [PERSON] et al. [10] proposed the N-point perspective problem, which is a PNP problem. The coordinates of N points in the target coordinate system, as well as those of the projection points on the image plane, are determined to solve the relative position and attitude relationship. [PERSON] et al. [11] proposed a method to generate a template according to the viewpoint and match real images to estimate the target pose. Sample templates, which include color and depth gradients, are obtained from different perspectives. The real image is matched with templates from different viewpoints, thresholds are set to obtain ideal matching results, and pose estimation is completed. The robust ICP algorithm is used to achieve 3D point set matching. [PERSON] performed pose measurement based on the target contour matching method [12]. Given an initial value, iterative optimization is used to minimize deviations by calculating the deviation between the result of the target edge reprojection and the actual imaging result of the target edge. The target pose is then solved. In this method, the target edge contour need to be obtained. [PERSON] et al. [13] adopted a line segment detector to extract line segment features, mean-shift and k-means++ approaches are applied to group line segments. This method recognized the lines corresponding to the fuselage and wing in the image to realize the structure extraction of straight wing aircraft. Studies on attitude external measurement of UAV based on multiple cameras have been conducted. [PERSON] proposed a method to solve aircraft attitudes by using the straight-line feature information of the aircraft [14]. The rotation matrix obtained by the angle bisector method is used as an initial value, and the coplanar error formula of the linear feature space of multiple camera images is established. The spatial coplanar error of all observed straight lines is used as an objective function to solve the optimal rotation matrix iteratively. Finally, the attitude parameters are analyzed. This calculation process requires the assistance of a theodolite. [PERSON] [15] proposed a method to compare simulated images with real ones and invert the target flight attitude.