基于工业三维检测的点云配准技术研究进展

王水仙; 邓朝晖; 葛吉民; 刘伟

doi:10.13394/j.cnki.jgszz.2022.0164

基于工业三维检测的点云配准技术研究进展

doi: 10.13394/j.cnki.jgszz.2022.0164

王水仙^{1, 3,},
邓朝晖^{2, 3, ,},
葛吉民^{1, 3},
刘伟^{1, 3}

1.
湖南科技大学机电工程学院, 湖南湘潭 411100
2.
华侨大学制造工程研究院, 福建厦门 361021
3.
湖南科技大学, 难加工材料高效精密加工湖南省重点实验室, 湖南湘潭 411100

基金项目: 湖南省自然科学省市联合基金（2021JJ50116）；湖南省高新技术产业科技创新引领计划（2020GK2003）；国家自然科学基金−浙江两化融合联合基金（U1809221）

详细信息

通讯作者:
邓朝晖，男，1968年生，博士、二级教授/博导。主要研究方向：智能磨削云平台、智能磨抛机器人、难加工材料绿色高效精密加工、新型超硬磨具制备及加工机理。E-mail：edeng0080@vip.sina.com

中图分类号: TG58; TP311; TP391
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- 文章访问数: 380
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- 被引次数: 0
出版历程
- 收稿日期: 2022-09-28
- 修回日期: 2023-01-17
- 录用日期: 2023-01-17
- 刊出日期: 2023-06-20

Research progress of point cloud registration technology based on industrial 3D inspection

WANG Shuixian^{1, 3
,},
DENG Zhaohui^{2, 3
, ,},
GE Jimin^{1, 3},
LIU Wei^{1, 3}

1.
Institute of Intelligent Manufacturing, Hunan University of Science and Technology, Xiangtan 411100, Hunan, China
2.
Manufacturing Engineering Research Institute, Huqiao University, Xiamen 361021, Fujian, China
3.
Hunan Provincial Key Laboratory of High Efficient and Precision Machining of Difficult-to-Cut Materials, Hunan University of Science and Technology, Xiangtan 411100, Hunan, China

摘要

摘要: 随着制造业的发展，所需零件逐渐向大尺寸、复杂形状、表面加工质量高等方向发展，且在加工过程中对零件质量进行检测是必不可少的环节。为提高质量检测的精度、速率以及自动化程度等，基于模型分析的三维检测取代了传统的手工检测和二维检测，成了工业检测领域的重要手段。点云配准作为三维检测中的关键环节，其精度直接影响检测结果的准确性。因此，对国内外学者在点云配准技术方面的主要研究成果进行综述，从算法原理出发，将目前的配准方法归纳为传统配准方法、基于仿生群智能优化算法的配准方法和基于深度学习的配准方法。详细介绍了各类方法的特点、优缺点、典型算法及其变体，总结了点云配准的技术难点并对未来的发展趋势进行了展望。
- 三维检测 /
- 点云配准 /
- 仿生群智能 /
- 深度学习
Abstract: With the development of the manufacturing industry, the required parts are gradually moving towards larger sizes, complex shapes, and high surface processing quality. Moreover, detecting the quality of parts during the processing is an essential step. In order to improve the accuracy, the speed and the automation of quality inspection, the 3D inspection based on model analysis has replaced the traditional manual inspection and the 2D inspection, and becomes an important means in the field of industrial inspection. The accuracy of point cloud registration, as a key part of 3D inspection, directly affects the accuracy of detection results. Therefore, the main research achievements of scholars at home and abroad in point cloud registration technology are summarized. Based on algorithm principles, the current registration methods are summarized into traditional registration methods, registration methods based on affine swarm intelligent optimization algorithms, and registration methods based on deep learning. The characteristics, the advantages and the disadvantages, the typical algorithms and their variants of each method are introduced in detail. The technical difficulties of point cloud registration are summarized and the future development trend is prospected.
- 3D inspection /
- point cloud registration /
- bionic swarm intelligence /
- deep learning

HTML全文

图 1 三维检测方法的流程图

Figure 1. Flow chart of 3D detection method

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图 2 最近点对应的准则

Figure 2. Guidelines corresponding to the nearest point

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图 3 4PCS算法的空间拓扑结构

Figure 3. Spatial topological structure of 4PCS algorithm

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图 4 3DSC的球型空间

Figure 4. Spherical space of 3DSC

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图 5 关键点及其邻域点的空间结构图

Figure 5. Spatial structure diagram of key points and their neighborhood points

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图 6 布谷鸟搜索算法的流程图

Figure 6. Flow chart of CSA

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图 7 人工蜂群算法的流程图

Figure 7. Flow chart of ABC algorithm

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图 8 PointNet网络结构图

Figure 8. Structure diagram of PointNet

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图 9 CNN网络结构图

Figure 9. Structure diagram of CNN

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表 1 各类算法的归纳表

Table 1. Summary table of various algorithms

分类	算法类型	代表算法	优势	劣势	适用范围
传统配准方法	全局搜索	ICP、4PCS等	精度高、运算简单	耗时、初始位姿要求高，容易陷入局部最优	精度要求较高，模型数据量较少，点云重叠率较高
	局部特征描述	PFH、FPFH、3DSC等	精度高、抗噪性强	运算复杂、耗时	点云局部特征明显，效率要求较低，也可应用于存在点云遮挡的模型的配准
	概率学统计	NDT、CPD等	抗噪性强、效率高	容易陷入局部最优	数据量庞大且配准精度要求较高
仿生群智能优化算法	布谷鸟搜索算法	CSA、MACSA、ICSA等	参数少、模型简单、效率高、全局搜索能力强	搜索方式具有盲目性，无法兼具全局搜索和局部寻优能力	简洁、快速、高精度的配准，也可应用于点云缺失、部分重合、点云遮挡的模型的配准
仿生群智能优化算法	人工蜂群算法	ABC、SAABC、SAABC等	算法灵活性强、全局搜索能力强、搜索性能好	搜索速度不均匀，表现为早熟收敛	简洁、快速、高精度的配准，也可应用于点云缺失、部分重合、点云遮挡的模型的配准
深度学习方法	端到端	PointNet、CNN、DeepVCP等	效率高、精度高、智能化程度高	对噪声和密度差敏感	配准精度要求很高，结构复杂，形状多样
深度学习方法	部分学习	PointNetLk、PCRNet、语义辅助正态分布变换等	灵活性强、收敛速度快、鲁棒性强	运算复杂	配准精度要求很高，结构复杂，形状多样

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基于工业三维检测的点云配准技术研究进展

doi: 10.13394/j.cnki.jgszz.2022.0164

通讯作者:
邓朝晖，男，1968年生，博士、二级教授/博导。主要研究方向：智能磨削云平台、智能磨抛机器人、难加工材料绿色高效精密加工、新型超硬磨具制备及加工机理。E-mail：edeng0080@vip.sina.com

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Research progress of point cloud registration technology based on industrial 3D inspection

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基于工业三维检测的点云配准技术研究进展

doi: 10.13394/j.cnki.jgszz.2022.0164

通讯作者: 邓朝晖，男，1968年生，博士、二级教授/博导。主要研究方向：智能磨削云平台、智能磨抛机器人、难加工材料绿色高效精密加工、新型超硬磨具制备及加工机理。E-mail：edeng0080@vip.sina.com

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Research progress of point cloud registration technology based on industrial 3D inspection

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通讯作者:
邓朝晖，男，1968年生，博士、二级教授/博导。主要研究方向：智能磨削云平台、智能磨抛机器人、难加工材料绿色高效精密加工、新型超硬磨具制备及加工机理。E-mail：edeng0080@vip.sina.com