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14 February 2020 Pose estimation for non-cooperative targets using 3D feature correspondences grouped via local and global constraints
Angfan Zhu, Jiaqi Yang, Zhiguo Cao, Li Wang, Yingying Gu
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Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114300G (2020) https://doi.org/10.1117/12.2538054
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China
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.
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Angfan Zhu, Jiaqi Yang, Zhiguo Cao, Li Wang, and Yingying Gu "Pose estimation for non-cooperative targets using 3D feature correspondences grouped via local and global constraints", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300G (14 February 2020); https://doi.org/10.1117/12.2538054
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KEYWORDS
Clouds
3D metrology
Data modeling
Galaxy groups and clusters
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