Binary Feature Description of 3D Point Cloud Based on Retina-like Sampling on Projection Planes

Yan, Zhiqiang; Wang, Hongyuan; Liu, Xiang; Ning, Qianhao; Lu, Yinxi

doi:10.3390/machines10110984

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

This is an early access version, the complete PDF, HTML, and XML versions will be available soon.

Open AccessArticle

Binary Feature Description of 3D Point Cloud Based on Retina-like Sampling on Projection Planes

^*,

and

Space Optical Engineering Research Center, Harbin Institute of Technology, Harbin 150001, China

Author to whom correspondence should be addressed.

Academic Editors: Shuai Li, Dechao Chen, Mohammed Aquil Mirza, Vasilios N. Katsikis, Dunhui Xiao and Predrag S. Stanimirovic

Machines 2022, 10(11), 984; https://doi.org/10.3390/machines10110984

Received: 3 September 2022 / Revised: 21 October 2022 / Accepted: 25 October 2022 / Published: 27 October 2022

(This article belongs to the Topic Intelligent Systems and Robotics)

Download PDF Citation Export

Abstract

A binary feature description and registration algorithm for a 3D point cloud based on retina-like sampling on projection planes (RSPP) are proposed in this paper. The algorithm first projects the point cloud within the support radius around the key point to the XY, YZ, and XZ planes of the Local Reference Frame (LRF) and performs retina-like sampling on the projection plane. Then, the binarized Gaussian density weight values at the sampling points are calculated and encoded to obtain the RSPP descriptor. Finally, rough registration of point clouds is performed based on the RSPP descriptor, and the RANSAC algorithm is used to optimize the registration results. The performance of the proposed algorithm is tested on public point cloud datasets. The test results show that the RSPP-based point cloud registration algorithm has a good registration effect under no noise, 0.25 mr, and 0.5 mr Gaussian noise. The experimental results verify the correctness and robustness of the proposed registration method, which can provide theoretical and technical support for the 3D point cloud registration application.

Keywords: 3D point cloud registration; point cloud feature description; retina-like sampling; binary descriptor

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share and Cite

MDPI and ACS Style

Yan, Z.; Wang, H.; Liu, X.; Ning, Q.; Lu, Y. Binary Feature Description of 3D Point Cloud Based on Retina-like Sampling on Projection Planes. Machines 2022, 10, 984. https://doi.org/10.3390/machines10110984

AMA Style

Yan Z, Wang H, Liu X, Ning Q, Lu Y. Binary Feature Description of 3D Point Cloud Based on Retina-like Sampling on Projection Planes. Machines. 2022; 10(11):984. https://doi.org/10.3390/machines10110984

Chicago/Turabian Style

Yan, Zhiqiang, Hongyuan Wang, Xiang Liu, Qianhao Ning, and Yinxi Lu. 2022. "Binary Feature Description of 3D Point Cloud Based on Retina-like Sampling on Projection Planes" Machines 10, no. 11: 984. https://doi.org/10.3390/machines10110984

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

Binary Feature Description of 3D Point Cloud Based on Retina-like Sampling on Projection Planes

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI