Abstract: Aiming at the problem of poor robustness of the current spatial noncooperative CubeSat pose estimation algorithm using image features, a design scheme using CubeSat vertices for pose estimation method is proposed in this paper. Based on the grayscale image obtained by binocular vision, HED (holistically nested edge detection) network and binary morphology processing method are used to improve the robustness of edge extraction. After detecting the polygon features in the edge image, the polygons with repeated and interference are filtered out, and the key vertices of the CubeSat are identified by a common side double frame key vertices discrimination algorithm, which realizes the estimation of the structural parameters and pose of the CubeSat. The CubeSat model is used for experimental verification. Compared with ICP (iterative closest point) precise registration method, the overall method achieves a maximum deviation of 4.4° and 1.2cm within the detection distance of 30~70cm. In this paper, the edge extraction method improves the discriminant accuracy of target structural parameters by 10%~40%, which provides a new idea for the estimation of structural parameters and pose of noncooperative target CubeSat.

Key words: noncooperative object, CubeSat, HED network, structural parameter estimation, pose estimation