Monocular visual estimation for autonomous aircraft landing guidance in unknown structured scenes

The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge, particularly regarding the effectiveness of solutions for monocular visual relative pose estimation. This study proposes a novel airborne monocular visual estimation method based on structured scene features to address this challenge. First, a multitask neural network model is established for segmentation, depth estimation, and slope estimation on monocular images. And a monocular image comprehensive three-dimensional information metric is designed, encompassing length, span, flatness, and slope information. Subsequently, structured edge features are leveraged to filter candidate landing regions adaptively. By leveraging the three-dimensional information metric, the optimal landing region is accurately and efficiently identified. Finally, sparse two-dimensional key point is used to parameterize the optimal landing region for the first time and a high-precision relative pose estimation is achieved. Additional measurement information is introduced to provide the autonomous landing guidance information between the aircraft and the optimal landing region. Experimental results obtained from both synthetic and real data demonstrate the effectiveness of the proposed method in monocular pose estimation for autonomous aircraft landing guidance in unknown structured scenes.