In this work, we propose a dynamic landing solution without the need for onboard exteroceptive sensors and an expensive computation unit, where all localization and control modules are carried out on the ground in a non-inertial frame. Our system starts with a relative state estimator of the aerial robot from the perspective of the landing platform, where the state tracking of the UAV is done through a set of onboard LED markers and an on-ground camera; the state is expressed geometrically on manifold, and is returned by Iterated Extended Kalman filter (IEKF) algorithm. Subsequently, a motion planning module is developed to guide the landing process, formulating it as a minimum jerk trajectory by applying the differential flatness property. Considering visibility and dynamic constraints, the problem is solved using quadratic programming, and the final motion primitive is expressed through piecewise polynomials. Through a series of experiments, the applicability of this approach is validated by successfully landing 18 cm x 18 cm quadrotor on a 43 cm x 43 cm platform, exhibiting performance comparable to conventional methods. Finally, we provide comprehensive hardware and software details to the research community for future reference.

翻译：本文提出了一种无需机载外部传感器及昂贵计算单元的动态降落方案，所有定位与控制模块均在非惯性系的地面端完成。系统首先构建基于降落平台视角的空中机器人相对状态估计器，通过机载LED标记与地面相机实现无人机状态追踪；该状态在流形上进行几何表示，并通过迭代扩展卡尔曼滤波算法返回。随后开发运动规划模块引导降落过程，利用微分平坦特性将其建模为最小加速度冲击轨迹。考虑可见性与动态约束，采用二次规划求解问题，最终运动基元以分段多项式表示。通过系列实验验证了该方法的适用性——成功在43厘米×43厘米平台上降落18厘米×18厘米四旋翼无人机，性能与传统方法相当。最后向研究社区提供全面的软硬件实现细节，以供后续参考。