Three-Dimensional Point Cloud Registration Method with Low Overlap Rate

Yuan, 张元 Zhang; Xiaoyan, 李晓燕 Li; Xie, 韩燮 Han

doi:10.3788/lop202158.0810014

激光与光电子学进展

2021

DOI: 10.3788/lop202158.0810014

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Three-Dimensional Point Cloud Registration Method with Low Overlap Rate

张元 Zhang Yuan¹,

李晓燕 Li Xiaoyan²,

韩燮 Han Xie³

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2024

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Low-Overlap Bullet Point Cloud Registration Algorithm Based on Line Feature Detection

Zhang,

Mu,

et al. 2024

Applied Sciences

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A bullet point cloud registration algorithm with a low overlap rate based on line feature detection was proposed to solve the problem of the difficulty and low efficiency of point cloud registration due to the low overlap rate among point clouds sampled by the bullet model. In this paper, voxel downsampling is used to remove some noise points and outliers from the bullet point cloud and applied to the specified resolution to reduce the calculation cost. The bullet point cloud is transformed to a better initial position by fitting the central axis with the geometrical features of the bullet. Then, the direction vector of the bullet linear features is obtained by using an icosahedral fitting discrete Hough transform to simplify the parameter space of the search transformation. Finally, the optimal rotation angle is searched for in the parameter space by using the improved Cuckoo algorithm to realize the registration of the bullet point cloud with a low overlap rate. Simulation and experimental results show that the proposed registration method can accurately register bullet point clouds of different densities with a low overlap rate. Compared with the commonly used ICP, GICP, and TRICP algorithms, the registration error of the proposed algorithm is reduced by 92.68% on average when the overlap rate is 52.85%. The registration error is reduced by 98.87% in the case of a 41.36% overlap rate, by 99.52% in the case of a 33.02% overlap rate, and by 98.89% in the case of a 22.75% overlap rate.

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Low-Overlap Bullet Point Cloud Registration Algorithm Based on Line Feature Detection

Zhang,

Mu,

et al. 2024

Applied Sciences

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Ldash：高鉴别力强鲁棒性的点云局部特征描述符

Zhou Lei,

Zhao Bao,

Liang Dong

et al. 2024

激光与光电子学进展

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一种融合形状与纹理的彩色点云配准算法

Zhang Yuan,

Shi Zepeng,

Pang Min

et al. 2024

Laser Optoelectron. Prog.

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Three-Dimensional Point Cloud Registration Method with Low Overlap Rate

Cited by 3 publications

References 0 publications

Low-Overlap Bullet Point Cloud Registration Algorithm Based on Line Feature Detection

Low-Overlap Bullet Point Cloud Registration Algorithm Based on Line Feature Detection

Ldash：高鉴别力强鲁棒性的点云局部特征描述符

一种融合形状与纹理的彩色点云配准算法

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