基于逐点前进法的改进型点云配准方法

李茂月; 许圣博; 孟令强; 刘志诚

doi:10.37188/CO.2023-0166

Article Contents

Chinese Optics > 2023 > Accepted Manuscript

LI Mao-yue, XU Sheng-bo, MENG Ling-qiang, LIU Zhi-cheng. An improved point cloud registration method based on the point-by-point forward method[J]. Chinese Optics. doi: 10.37188/CO.2023-0166

Citation:

LI Mao-yue, XU Sheng-bo, MENG Ling-qiang, LIU Zhi-cheng. An improved point cloud registration method based on the point-by-point forward method[J]. Chinese Optics. doi: 10.37188/CO.2023-0166

LI Mao-yue, XU Sheng-bo, MENG Ling-qiang, LIU Zhi-cheng. An improved point cloud registration method based on the point-by-point forward method[J]. Chinese Optics. doi: 10.37188/CO.2023-0166

Citation:

LI Mao-yue, XU Sheng-bo, MENG Ling-qiang, LIU Zhi-cheng. An improved point cloud registration method based on the point-by-point forward method[J]. Chinese Optics. doi: 10.37188/CO.2023-0166

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An improved point cloud registration method based on the point-by-point forward method

doi: 10.37188/CO.2023-0166

The Key Laboratory of Advanced Manufacturing and Intelligent Technology of the Ministry of Education, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China

Funds: Supported by National Natural Science Foundation of China (No. 51975169), Natural Science Foundation of Heilongjiang Province (No. LH2022E085)

More Information

Corresponding author: lmy0500@163.com
Available Online: 07 Nov 2023

Abstract

Abstract

To improve both the efficiency and accuracy of point cloud registration, this study proposed an improved method based on point-by-point advance feature point extraction. Firstly, the point-by-point advance method extracts point cloud feature points rapidly, and greatly reduces the number of point clouds, while retaining the characteristics of the point cloud model. The KN-4PCS algorithm, using normal vector constraints, conducts a preliminary registration of the source and target point cloud. Finally, the fine registration is achieved with the two-way Kd-tree optimized LM-ICP algorithm. In the open point cloud data registration experiment of Stanford University, the average error is reduced by about 70.2% compared with the SAC-IA+ICP algorithm, and the registration time is reduced by about 86.2% and 81.9%, respectively. The algorithm maintains high accuracy and low time consumption even with varying degrees of Gaussian noise. In the point cloud registration experiment of indoor objects, the average registration error was measured to be 0.0742 mm with an average algorithm time of 0.572 s. The comparison and analysis of Stanford open data and real indoor scene object point cloud data shows that this method can effectively improve the efficiency, accuracy, and robustness of point cloud registration. Furthermore, this study establishes a strong foundation for indoor target recognition and pose estimation through the point cloud.
- point cloud registration,
- KN-4PCS,
- bidirectional Kd-tree,
- LM-ICP

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References(20)

References

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