针对艇载无人机单视觉定位易受海洋环境干扰影响，自主降落时近艇端极易出现标识码丢失、定位误差和死区较大的问题，提出一种海空双视觉协同融合定位的无人艇载无人机自主降落方法。通过在无人机和无人艇起降平台上搭载摄像头，利用无人艇降落区域和无人机底端布放的合作标识码，进行海空双视觉协同导航，同时采用Kalman滤波算法预估目标位置，不仅可以提高识别的效率，还可对目标短暂丢失情况进行位置预测，并在海空视觉协同精确定位的基础上结合PID控制算法实现无人艇载无人机的自主降落控制。仿真实验和海上实验结果表明了本文提出的基于海空协同的视觉协同融合定位算法和自主降落控制方法的可行性和有效性，为异构类子母式海空机器人系统的自主回收提供了一种有效的解决方案。

Aiming at the problems that the single vision positioning of the boat-mounted UAV is easy to be affected by the interference of marine environment, and it is very easy to have identification code loss, positioning error and large dead zone at the near-boat during autonomous landing, an unmanned boat-mounted UAV autonomous landing method with the cooperative fusion of sea and air vision positioning was proposed. By carrying cameras on the UAV and the UAV landing platform and using the cooperative identification codes laid on the landing area of the UAV and the bottom of the UAV, the co-operative navigation of sea-air dual vision was carried out, while the Kalman filtering algorithm was used to predict the target position, which not only improved the efficiency of identification but also predicted the position of the target in case of brief loss, and on the basis of the co-operative precise positioning of sea-air vision combined with PID, the unmanned boat-mounted UAV can be controlled by the PID control algorithm. The results of simulation experiments and at-sea experiments demonstrate the feasibility and effectiveness of the visual cooperative fusion localization algorithm and autonomous landing control method based on air-sea cooperation proposed in this paper, which can provide an effective solution for the autonomous recovery of heterogeneous class of submarine air-sea robotic systems.