Robust Monocular Relative Pose Estimation for In-Flight Wingtip Docking in a Chained-Wing UAV System

In-flight wingtip docking can connect multiple UAVs into a high-aspect-ratio chained-wing configuration, offering potential improvements in aerodynamic efficiency, endurance, and cruise performance. Reliable close-range 6-DoF relative pose estimation is essential for precise docking; however, existing studies have focused primarily on aerodynamic characteristics, docking mechanisms, and guidance and control, while robust monocular pose estimation under partial occlusion and image degradation remains insufficiently investigated. To address this gap, a cooperative-target-based monocular vision method is proposed for six-degree-of-freedom relative pose estimation during in-flight wingtip docking in a chained-wing UAV system. An asymmetric seven-ring planar cooperative target is designed to reduce feature-identification ambiguity and retain sufficient geometric constraints under partial occlusion. The front end combines YOLOv8n-seg instance segmentation with local inner–outer ring refinement and topology-based feature classification. According to target visibility, the back end adaptively selects seven-, five-, or four-point pose-estimation modes, refines valid solutions by minimizing reprojection error, and removes isolated suspicious candidates when necessary. The method is evaluated on a controlled indoor hardware-in-the-loop platform as a pre-flight assessment of its incremental measurement performance. In controlled incremental-response experiments, the mean absolute adjacent-increment errors do not exceed 0.08mm in translation and 0.13° in rotation. The complete method achieves a pose-solving success rate of 99.07%. Experiments involving progressive wing occlusion and synthetic directional motion blur further demonstrate that the method can provide stable and continuous relative pose output under challenging observation conditions.