In this work, we propose a novel dynamic landing solution utilizing an on-ground sensor suite, eliminating the need for airborne exteroceptive sensors and expensive computational units. All localization and control modules operate in a noninertial frame. The system begins with a relative state estimator that tracks the unmanned aerial vehicle’s (UAV) state via onboard light-emitting diode (LED) markers and an on-ground camera. The state is geometrically expressed on a manifold and estimated using an iterated extended Kalman filter (IEKF) algorithm. A motion planning module is then developed to guide the landing process, leveraging the differential flatness property to formulate it as a minimum jerk trajectory. Considering visibility and dynamic constraints, the problem is solved using quadratic programming (QP), with the final motion primitive represented through piecewise polynomials. A series of experiments validate the applicability of the proposed approach, achieving successful landings of an $18\times 18$ cm quadrotor on a $43\times 43$ cm platform, demonstrating performance comparable to conventional methods. In addition, comprehensive hardware and software details are provided for future reference within the research community.