Pose estimation of non-cooperative targets based on docking surface

Wenkai Du; Zongming Liu; Yu Zhang; Shuqing Cao; Nong Sang

doi:10.1117/12.2176726

20 April 2015 Pose estimation of non-cooperative targets based on docking surface

Wenkai Du, Zongming Liu, Yu Zhang, Shuqing Cao, Nong Sang

Proceedings Volume 9477, Optical Pattern Recognition XXVI; 94770L (2015) https://doi.org/10.1117/12.2176726
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

In space rendezvous and docking missions, pose estimation of non-cooperative targets is challenging as priori information about motion and structure are unknown. The extraction and recognition are far more difficult conducted on a whole target. To solve this problem, a pose estimation method based on docking surface is proposed. The docking surfaces have more sophisticated structure with similar appearance among different countries than other surfaces. So docking surface is easy to automatically recognize or manually mark in images. In this paper, a control point representing mark information is chosen to assist with docking surface detection. The vertices of docking surface can be used to estimate pose. Firstly, binocular images are obtained by 3-D simulation technology. Then, a novel framework is proposed to detect edges of the docking surface in each image. Specifically, we detect lines in an image and group them according to the slopes. The control point is utilized to pick out the edges from the lines detected. Finally, the pose of the target is calculated by the four vertices of the docking surface. Simulation result shows that the position errors and attitude errors meet the requirement of pose estimation. The method provides a new solution for pose estimation of the non-cooperative target, and has potential significance for space rendezvous and docking technology.

Citation Download Citation

Wenkai Du, Zongming Liu, Yu Zhang, Shuqing Cao, and Nong Sang "Pose estimation of non-cooperative targets based on docking surface", Proc. SPIE 9477, Optical Pattern Recognition XXVI, 94770L (20 April 2015); https://doi.org/10.1117/12.2176726

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