Estimation of Point Cloud Object Pose Using Particle Swarm Optimization-Reference-Cited by-同舟云学术

Estimation of Point Cloud Object Pose Using Particle Swarm Optimization

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the International Conference on Machine Vision and Applications - ICMVA 2018
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Author:

Yu Ge¹,Liu Ming²,Liu Tianyu³,Guo Lili¹

Affiliation:

1. Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China

2. Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China

3. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China

Funder

Techonology and Engineering Center for Space Utilization, Chinese Academy of Sciences

National Natural Science Foundation of China

Publisher

ACM Press

Reference35 articles.

1. Dirk Buchholz. 2016. 3D Point Cloud Based Pose Estimation. In Bin Picking. Springer, 13--37.

2. Jay M Wong, Vincent Kee, Tiffany Le, Syler Wagner, Gian-Luca Mariottini, Abra- ham Schneider, Lei Hamilton, Rahul Chipalkatty, Mitchell Hebert, David Johnson, et al. 2017. SegICP: Integrated Deep Semantic Segmentation and Pose Estimation. arXiv preprint arXiv:1703.01661 (2017).

3. Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker, Alberto Rodriguez, and Jianxiong Xiao. 2017. Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge. In Robotics and Automation (ICRA), 2017 IEEE International Conference on. IEEE, 1386--1383.

4. R. B Rusu, N Blodow, and M Beetz. 2009. Fast Point Feature Histograms (FPFH) for 3D registration. In IEEE International Conference on Robotics and Automation. 1848--1853.

5. Jiaqi Yang, Zhiguo Cao, and Qian Zhang. 2016. A fast and robust local descriptor for 3D point cloud registration. Information Sciences 346 (2016), 163--179.

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