Vision-guided autonomous landing technology for noncooperative target

Traditional vision-guided aircraft autonomous landing technology relies on artificial design features such as cooperative markers. In complex environments, these systems are unreliable. For the above problems, this paper proposes that vision-guided autonomous landing technology for non-cooperative target, which utilizes the geometric features of the target without relying on the cooperative target. The system first extracts 2D key points under the object and key point detection based on deep learning, and then solves the Perspective-n-Point problem to obtain the pose. The aircraft can landing by taking images and calculate the pose steadily. In this paper, a visual guidance system composed of hardware such as UAV and camera is used for experiments. Experiments show that the system can successfully achieve visual guided landing. Pose update frequency is higher than 10 Hz, the average error of key point prediction is less than 2.5 pixels, and the reprojection error is less than 4 pixels.