Improved Iterative Closest Point(ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration

Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational efficiency due to the closest point computation. Most implementations of the ICP algorithm attempt to deal with this issue by modifying correspondence or adding coarse registration. However, this leads to sacrificing the accuracy rate or adding the algorithm complexity. This paper proposes a hierarchical optimization approach that includes improved voxel filter and Multi-Scale Voxelized Generalized-ICP (MVGICP) for 3D point cloud registration. By combining traditional voxel sampling with point density, the outlier filtering and downsample are successfully realized. Through multi-scale iteration and avoiding closest point computation, MVGICP solves the local minimum problem and optimizes the operation efficiency. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of outlier filtering and registration performance.

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A Modified Iterative Closest Point Algorithm for 3D Point Cloud Registration

Computer-Aided Civil and Infrastructure Engineering ◽

10.1111/mice.12184 ◽

2016 ◽

Vol 31 (7) ◽

pp. 515-534 ◽

Cited By ~ 27

Author(s):

Roberto Marani ◽

Vito Renò ◽

Massimiliano Nitti ◽

Tiziana D'Orazio ◽

Ettore Stella

Keyword(s):

Point Cloud ◽

Iterative Closest Point ◽

3D Point Cloud ◽

Point Cloud Registration ◽

Iterative Closest Point Algorithm

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Automatic 3D point cloud registration algorithm based on triangle similarity ratio consistency

IET Image Processing ◽

10.1049/iet-ipr.2019.1087 ◽

2020 ◽

Vol 14 (14) ◽

pp. 3314-3323

Author(s):

Xuyan Zou ◽

Hanwu He ◽

Yueming Wu ◽

Youbin Chen ◽

Mingxi Xu

Keyword(s):

Point Cloud ◽

3D Point Cloud ◽

Point Cloud Registration ◽

Registration Algorithm ◽

Similarity Ratio

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Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

Journal of Computer-Aided Design & Computer Graphics ◽

10.3724/sp.j.1089.2018.16472 ◽

2018 ◽

Vol 30 (4) ◽

pp. 642

Author(s):

Guichao Lin ◽

Yunchao Tang ◽

Xiangjun Zou ◽

Qing Zhang ◽

Xiaojie Shi ◽

...

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Point Cloud ◽

Gaussian Mixture ◽

Point Cloud Registration ◽

Registration Algorithm

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OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽

10.3390/ma14061563 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1563

Author(s):

Ruibing Wu ◽

Ziping Yu ◽

Donghong Ding ◽

Qinghua Lu ◽

Zengxi Pan ◽

...

Keyword(s):

Additive Manufacturing ◽

Point Cloud ◽

Point Clouds ◽

Iterative Closest Point ◽

Multiple Point ◽

Icp Algorithm ◽

Registration Algorithm ◽

Cloud Models ◽

Comparison Results ◽

Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

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