无人机空中回收视觉导航技术

针对无人机空中回收过程中的导航问题，提出一种利用深度学习进行目标检测并配合双目视觉进行位姿 估计的技术。设计空中回收视觉导航系统，通过改进原有目标检测算法YOLOv3 框架提高回收过程中的检测精度和 速度；通过双目视觉系统对特征点进行3 维位姿解算，返回无人机和回收锥套中心相对位置信息。实验结果表明： 改进后的检测算法平均精度比YOLOv3 提高了3.2%，检测速度提高到73 FPS，检测速度明显提升；双目视觉算法 的位姿解算精确度高，两者同时满足导航系统精确性和实时性的要求。

Vision Navigation Technology for UAV Aerial Recovery

Aiming at the navigation problem of unmanned aerial vehicle (UAV) in the process of aerial recovery, a technology of target detection based on deep learning and pose estimation based on binocular vision is proposed. A visual navigation system for aerial recovery is designed, which improves the detection accuracy and speed in the recovery process by improving the original target detection algorithm YOLOv3 framework. The 3D pose of the feature points is calculated by the binocular vision system, and the relative position information of the UAV and the recovery drogue center is returned. The experimental results show that the average accuracy of the improved algorithm is 3.2% higher than that of YOLOv3, and the detection speed is increased to 73 FPS, which shows that the detection speed is significantly improved. The pose calculation accuracy of the binocular vision algorithm is high, and both of them meet the requirements of accuracy and real-time of navigation system.