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Proceedings Paper

Pose estimation for non-cooperative targets using 3D feature correspondences grouped via local and global constraints
Author(s): Angfan Zhu; Jiaqi Yang; Zhiguo Cao; Li Wang; Yingying Gu
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Paper Abstract

The pose of a non-cooperative target represented by point cloud can be estimated through point cloud registration, which is generally performed by searching good correspondences. Seeking correspondences in the context of non-cooperative target pose estimation is a challenging task due to the low texture, noise and occlusion, resulting in a number of outliers in the initial correspondences. In order to gain a high quality set of feature correspondences, we employ a combination of local and global constraints to remove the outliers in initial correspondences. On a local scale, we use simple and low-level geometric invariants. On a global scale, we apply covariant constraints for finding compatible correspondences. In the experiments, we use four groups of different non-cooperative targets to evaluate our algorithm and the results verify that the quality of the correspondence set has been greatly improved by our method and the pose can be accurately estimated.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300G (14 February 2020); doi: 10.1117/12.2538054
Show Author Affiliations
Angfan Zhu, Huazhong Univ. of Science and Technology (China)
Jiaqi Yang, Northwestern Polytechnical Univ. (China)
National Engineering Lab. for Integrated Aero-Space-Ground-Ocean Big Data Application Technology (China)
Zhiguo Cao, Huazhong Univ. of Science and Technology (China)
Li Wang, Beijing Institute of Control Engineering (China)
Yingying Gu, Beijing Institute of Control Engineering (China)

Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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