Wing Deformation-Aware Vision-Based Airplane Attitude Measurement

Attitude measurement is significant for airplane flight tests, especially in the landing phase. Among various technologies, off-board vision-based monocular measurement methods are flexible, nonintrusive, and self-contained. Previously employed model-based attitude measurement methods assume that airplanes are rigid objects with fixed 3-D models. However, airplane wing deformation caused by aerodynamic loads is neglected, which decreases the pose accuracy. To address it, this article proposes a novel method of estimating wing deformations and measuring airplane attitudes. It begins with building a polynomial wing deformation model using real wing data. Then, the wing deformation is reconstructed by leveraging dense correspondences between the input Red Green Blue color mode (RGB) image and the rendered image corresponding to a coarse pose estimation. After rectifying the wing deformation, a two-stage pose refinement (PR) strategy is proposed to refine the coarse pose. To evaluate our method, we propose a new airplane image dataset in which wing deformations are manually annotated. Experiments on this dataset demonstrate the outperformance of our method. Furthermore, results on airplane flight tests illustrate that our method can run on images of resolution $1920\times 1080$ with a speed of 21 Hz and an accuracy of 0.4° for the three attitude angles.