Aerial manipulation (AM) promises to move Unmanned Aerial Vehicles (UAVs) beyond passive inspection to contact-rich tasks such as grasping, assembly, and in-situ maintenance. Most prior AM demonstrations rely on external motion capture (MoCap) and emphasize position control for coarse interactions, limiting deployability. We present a fully onboard perception-control pipeline for contact-rich AM that achieves accurate motion tracking and regulated contact wrenches without MoCap. The main components are (1) an augmented visual-inertial odometry (VIO) estimator with contact-consistency factors that activate only during interaction, tightening uncertainty around the contact frame and reducing drift, and (2) image-based visual servoing (IBVS) to mitigate perception-control coupling, together with a hybrid force-motion controller that regulates contact wrenches and lateral motion for stable contact. Experiments show that our approach closes the perception-to-wrench loop using only onboard sensing, yielding an velocity estimation improvement of 66.01% at contact, reliable target approach, and stable force holding-pointing toward deployable, in-the-wild aerial manipulation.

翻译：空中操控（AM）有望将无人机（UAV）的应用从被动检测扩展到抓取、装配和原位维护等接触密集型任务。大多数先前的AM演示依赖于外部运动捕捉（MoCap）系统，并强调用于粗略交互的位置控制，这限制了其可部署性。我们提出了一种用于接触密集型AM的完全机载感知-控制流程，无需MoCap即可实现精确的运动跟踪和受控的接触力旋量。其主要组成部分包括：（1）一种增强的视觉惯性里程计（VIO）估计器，它包含仅在交互期间激活的接触一致性因子，从而收紧接触坐标系周围的不确定性并减少漂移；（2）基于图像的视觉伺服（IBVS）以减轻感知-控制耦合，以及一个混合力-运动控制器，该控制器用于调节接触力旋量和横向运动以实现稳定接触。实验表明，我们的方法仅使用机载传感即可闭合从感知到力旋量的控制回路，在接触时实现了66.01%的速度估计改进，实现了可靠的目标接近和稳定的力保持——这指向了可部署的、野外环境下的空中操控。