Point cloud registration method of rape branches based on ISS-LCG combined feature points

XIE Zhonghong; HUANG Yifan; WU Chongyou

doi:10.7671/j.issn.1001-411X.202205019

Journal of South China Agricultural University > 2023 > 44(3): 456-463. > DOI: 10.7671/j.issn.1001-411X.202205019

XIE Zhonghong, HUANG Yifan, WU Chongyou. Point cloud registration method of rape branches based on ISS-LCG combined feature points[J]. Journal of South China Agricultural University, 2023, 44(3): 456-463. DOI: 10.7671/j.issn.1001-411X.202205019

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Point cloud registration method of rape branches based on ISS-LCG combined feature points

1.
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
2.
Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210049, China

More Information

Received Date: May 10, 2022
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
Aiming at the problems of low accuracy and slow speed of traditional registration methods, we took point cloud of rape (Brassica napus L.) branches as the research object, and proposed a registration method based on ISS-LCG combined feature points.

Method
The pods of mature rape branches were taken as the research object. The background noise of rape point cloud was removed to obtain the clear and complete point cloud of rape branches. Intrinsic shape signatures (ISS) algorithm was used to extract feature points of point cloud. Linear congruential generator (LCG) algorithm was used to pseudo-randomly select some points of point cloud to constitute key points. Feature points and key points were combined to form ISS-LCG combined feature points. Then, the combined feature points were described by 3D shape context (3DSC) algorithm. Finally, RANSAC + ICP two-step point cloud registration method was used for point cloud registration.

Result
The precision of on-time registration of rape branch point cloud in pairwise matching was the highest among shooting angles with an interval of 30°. The registration error was about 0.066 mm. Compared with the method without combined feature points, the registration accuracy was improved by 50%−70%. The registration time was less than 48 s, and the average registration time was 8.706 s.

Conclusion
The proposed method could achieve highly precise and efficient automatic registration of mature rape plants in a controlled environment.
- Brassica napus L.,
- Maturation stage,
- ISS-LCG combined feature point,
- RANSAC algorithm,
- Point cloud registration

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References (23)

References

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Point cloud registration method of rape branches based on ISS-LCG combined feature points