Precision landing remains a critical challenge for unmanned aerial vehicles (UAVs), as inaccurate landings may lead to potential damage and operational risks. To overcome the limitations of GPS-only and computation-intensive vision-based approaches, this paper developing an autonomous precision landing system that integrates GPS with ArUco marker–based computer vision. The UAV is equipped with a monocular camera to detect and track a landing marker, while a vision-based algorithm estimates the marker’s pose to determine the UAV’s relative position and orientation. Based on this information, a closed-loop control algorithm continuously adjusts the UAV position to achieve accurate landing on the target point. Our work is validated in the Gazebo simulation environment and further verified on a real UAV platform, demonstrating substantially improved landing accuracy relative to GPS-only–based landing.