无人机空中回收视觉导航技术  

Vision Navigation Technology for UAV Aerial Recovery

作　　者：闫留浩 袁锁中[1] Yan Liuhao;Yuan Suozhong(Key Laboratory of Navigation,Control and Health Management Technologies of Advanced Aircraft,Ministry of Industry and Information Technology,College of Automation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学自动化学院先进飞行器导航、控制与健康管理工业和信息化部重点实验室,南京211106

出　　处：《兵工自动化》2022年第12期56-60,共5页Ordnance Industry Automation

基　　金：国家自然科学基金(61273050);中央高校基本科研业务费资助(XCA18155)。

摘　　要：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73FPS,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。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.

关 键 词：空中回收 YOLOv3 双目视觉 位姿解算 

分 类 号：V279[航空宇航科学与技术—飞行器设计]