An image appearance based optimization scheme for monocular 6D pose estimation of SOR cabins

Abstract

Monocular pose estimation for the surface of revolution (SOR) structure is a classical computer vision issue in aerospace applications. Existing pose estimation methods solve the 3-DoF translation and 2-DoF attitude reliably. However, they still require external constraints to solve the remained 1-DoF rotation and optimize the full 6D pose. This work proposes a general full 6D pose estimation method based on the image appearance feature and a non-linear optimization scheme. First, the position and attitude are solved from the imaged elliptic cross-sections. Then, an error objective function is designed to describe the appearance difference of the cross section caused by the pose error. Finally, the attitude is determined, and the full pose is solved by sorting and iteratively minimizing the objective function. Experiment results show that the proposed method is accurate, and is adaptive to view changes, dark illumination, and partial occlusion conditions. The average absolute translation and rotation error is 0.434 mm and 0.661° respectively, outperforming the keypoint-based baseline. The mean image difference on real images is 0.306, yielding a 17% error reduction from that of the fiducial marker benchmark.