Monocular pose estimation for vision-guided fixed-wing aircraft airport landing

With the rapid development of the low-altitude economy, the intelligence level of aircraft urgently needs improvement. In emergency landing scenarios, traditional guidance systems are prone to communication failures, adverse weather, or complex environmental interference, posing significant safety risks. To address the high-precision pose estimation requirements for autonomous aircraft landing, this paper proposes a monocular vision-based sparse keypoint parameterization method. Our approach employs a deep neural network to rapidly detect landing-area keypoints, establishes 2D-3D sparse correspondences, and leverages Perspective-n-Point (PnP) for real-time pose solving, overcoming the limitations of traditional systems that rely on prior calibration or cooperative markers. Experiments demonstrate that our method achieves robust pose estimation even under extreme conditions (e.g., signal interference, low visibility), providing reliable technical support for emergency landings. A computationally efficient and interference-resistant sparse keypoint representation for targets. And a cooperative-marker-free monocular vision landing guidance system, significantly enhancing landing safety in complex scenarios.