The tracking and measurement methods of drogue in autonomous aerial refueling which based 2D image, because of the loss of depth information, are susceptible to light changes, occlusion, etc., resulting in false tracking and the failure of position and orientation of drogue measurement, which may lead to collision of drones. In order to solve these problems, this paper intends to introduce 3D information to solve the position and orientation of drogue based on the original 2D image tracking method. The method mainly includes two parts, tracking and measurement algorithms, using the decision function to effectively combine the two algorithms. The tracking algorithm provides a positioning area for the measurement algorithm, thereby reducing the amount of measurement operation data. The 3D measurement result of the measurement algorithm based on stereo vision is used for measuring the drogue's position and orientation, and fed back to the tracking algorithm, which eliminates the candidate area and reduces the error rate to improve accuracy of the tracking algorithm. The measurement algorithm can still be realized stably high-precision under occlusion and no cooperation markers, which is not available in traditional 2D image-based methods. We perform semi-physical simulation experiments to verify the effectiveness of the proposed method

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 in autonomous refueling or formation flight.