Discriminative optimization algorithm with global–local feature for LIDAR point cloud registration-Reference-Cited by-同舟云学术

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Discriminative optimization algorithm with global–local feature for LIDAR point cloud registration

Published:2021-10-27 Issue:23 Volume:42 Page:9003-9023
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Wang Jia¹^ORCID,Wang Ping¹,Li Biao¹,Fu Ruigang¹,Zhao Siyi²,Zhang Hong³

Affiliation:

1. College of Electronic Science and Technology, National University of Defense Technology, Changsha, China

2. College of Computer Science and Technology, National University of Defense Technology, Changsha, China

3. NO 702 Research Institute, China Ship Scientific Research Center, Wuxi, China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2021.1975843

Reference40 articles.

1. Ao, S., H. Qingyong, B. Yang, A. Markham, and Y. Guo. 2021. “SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Virtual, 11753-11762

2. Aoki, Y., H. Goforth, R. A. Srivatsan, and S. Lucey. 2019. “Pointnetlk: Robust and Efficient Point Cloud Registration Using Pointnet.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, 7163–7172.

3. Bayro-Corrochano, E., and J. Ortegon-Aguilar. 2004. “Lie Algebra Template Tracking.” In Proceedings of the IEEE 17th International Conference on Pattern Recognition, Cambridge, 2:56-59.

4. Lie algebra approach for tracking and 3D motion estimation using monocular vision

5. A method for registration of 3-D shapes

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