基于主成分分析和特征图匹配的点云配准方法

郑伟斌; 练国富; 张学明; 郭方

doi:10.11959/j.issn.2096-6652.202340

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基于主成分分析和特征图匹配的点云配准方法

学术论文 | 更新时间：2024-04-07

- 基于主成分分析和特征图匹配的点云配准方法
- Point cloud registration method based on principal component analysis and feature map matching
- 智能科学与技术学报 2023年5卷第4期页码：543-552
- 作者机构：
  
  1.福建理工大学机械与汽车工程学院，福建福州 350118
  2.福建理工大学计算机科学与数学学院，福建福州 350118
- 作者简介：
  
  [ "郑伟斌（1998- ），男，福建理工大学机械与汽车工程学院硕士生，主要研究方向为计算机视觉与三维重建。" ]
  [ "练国富（1980- ），男，博士，福建理工大学机械与汽车工程学院教授，主要研究方向为增材制造技术与机器视觉。" ]
  [ "张学明（1996- ），男，福建理工大学机械与汽车工程学院硕士生，主要研究方向为计算机视觉与深度学习。" ]
  [ "郭方（1977- ），男，博士，福建理工大学计算机科学与数学学院副教授，主要研究方向为计算机视觉、大数据处理与分析。" ]
- 基金信息：
  
  福建省科技重大专项专题项目(2020HZ03018)
- DOI：10.11959/j.issn.2096-6652.202340
  中图分类号： TP391
- 收稿：2022-12-07，
  
  修回：2023-06-02，
  
  纸质出版：2023-12-15
- 稿件说明：
移动端阅览
郑伟斌, 练国富, 张学明, 等. 基于主成分分析和特征图匹配的点云配准方法[J]. 智能科学与技术学报, 2023,5(4):543-552.

ZHENG Weibin, LIAN Guofu, ZHANG Xueming, et al. Point cloud registration method based on principal component analysis and feature map matching[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 543-552.
郑伟斌, 练国富, 张学明, 等. 基于主成分分析和特征图匹配的点云配准方法[J]. 智能科学与技术学报, 2023,5(4):543-552. DOI： 10.11959/j.issn.2096-6652.202340.

ZHENG Weibin, LIAN Guofu, ZHANG Xueming, et al. Point cloud registration method based on principal component analysis and feature map matching[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 543-552. DOI： 10.11959/j.issn.2096-6652.202340.

摘要

由于点云模型存在不同程度的重叠，点云配准容易出现特征匹配错误、配准难度大等问题。因此，提出了一种基于主成分分析和特征图匹配的点云配准方法。配准前，首先采用带主轴校正的主成分分析方法进行点云初始位姿调整，建立KD树进行重叠区域搜索。其次，根据两幅点云的重叠区域计算采样点的快速点特征直方图特征，进行点云特征图匹配以及裁剪迭代最近点精配准。在现有数据集以及实际扫描模型上进行配准实验，实验结果表明该方法的稳定性好，精度更高，相较于其他算法精度能提高25%以上。

Abstract

Due to varying degrees of overlap in point cloud models

point cloud registration is prone to problems

such as feature matching errors and high difficulty in registration. Therefore

a point cloud registration method based on principal component analysis and feature map matching is proposed. Before registration

the principal component analysis method with spindle correction was used to adjust the initial pose

then the K-dimensional tree was established to search the overlapping area. Secondly

the fast point feature histograms features of the sampling points were calculated according to the overlapping area of the two-point cloud

and the point cloud feature graph matching and trimmed iterative closest point (TrICP) fine registration were performed. Registration experiments were carried out according to the existing datasets and the actual scanning model. The experimental results show that the method has good stability and higher accuracy

and the accuracy can be improved by more than 25% compared with other algorithms.

关键词

Keywords

references

李博洋 , 刘思健 , 崔明月 , 等 . 基于最小回环检测的多车协同SLAM框架 [J ] . 电子学报 , 2021 , 49 ( 11 ): 2241 - 2250 .

LI B Y , LIU S J , CUI M Y , et al . Multi-vehicle collaborative SLAM framework for minimum loop detection [J ] . Acta Electronica Sinica , 2021 , 49 ( 11 ): 2241 - 2250 .

DONG Z , LIANG F X , YANG B S , et al . Registration of large-scale terrestrial laser scanner point clouds: a review and benchmark [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2020 , 163 : 327 - 342 .

BESL P J , MCKAY N D . Method for registration of 3-D shapes [C ] // Proceedings of SPIE 1611 , Sensor Fusion IV : Control Paradigms and Data Structures. Washington: SPIE , 1992 , 1611 : 586 - 606 .

ZHONG L S , YING J , YANG H M , et al . Triple screening point cloud registration method based on image and geometric features [J ] . Optik , 2021 , 246 : 167763 .

张旭春 , 周洪军 , 郑津津 , 等 . 基于多尺度特征及点距离约束的点云配准 [J ] . 激光与光电子学进展 , 2021 , 58 ( 24 ): 325 - 333 .

ZHANG X C , ZHOU H J , ZHENG J J , et al . Point cloud registration based on multi-scale features and point distance constraints [J ] . Laser & Optoelectronics Progress , 2021 , 58 ( 24 ): 325 - 333 .

XU G X , PANG Y J , BAI Z X , et al . A fast point clouds registration algorithm for laser scanners [J ] . Applied Sciences , 2021 , 11 ( 8 ): 3426 .

ZAGANIDIS A , MAGNUSSON M , DUCKETT T , et al . Semantic-assisted 3D normal distributions transform for scan registration in environments with limited structure [C ] // Proceedings of 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Piscataway : IEEE Press , 2017 : 4064 - 4069 .

荆路 , 武斌 , 李先帅 . 基于SAC-IA和NDT融合的点云配准方法 [J ] . 大地测量与地球动力学 , 2021 , 41 ( 4 ): 378 - 381 .

JING L , WU B , LI X S . Point cloud registration method based on SAC-IA and NDT fusion [J ] . Journal of Geodesy and Geodynamics , 2021 , 41 ( 4 ): 378 - 381 .

杜辉 , 郑长亮 , 苗春雨 , 等 . 一种动态特征匹配的部分重叠点云配准方法 [J ] . 电信科学 , 2021 , 37 ( 4 ): 97 - 107 .

DU H , ZHENG C L , MIAO C Y , et al . A partial overlapping point cloud registration method based on dynamic feature matching [J ] . Telecommunications Science , 2021 , 37 ( 4 ): 97 - 107 .

李鑫 , 莫思特 , 黄华 , 等 . 自动计算重叠度的多源点云配准方法 [J ] . 红外与激光工程 , 2021 , 50 ( 12 ): 260 - 268 .

LI X , MO S T , HUANG H , et al . Multi-source point cloud registration method for automatically calculating overlap degree [J ] . Infrared and Laser Engineering , 2021 , 50 ( 12 ): 260 - 268 .

LI J , QIAN F , CHEN X F . Point cloud registration algorithm based on overlapping region extraction [J ] . Journal of Physics: Conference Series , 2020 , 1634 ( 1 ): 012012 .

张元 , 李晓燕 , 韩燮 . 一种低重叠率的三维点云配准方法 [J ] . 激光与光电子学进展 , 2021 , 58 ( 8 ): 162 - 171 .

ZHANG Y , LI X , HAN X . Three-dimensional point cloud registration method with low overlap rate [J ] . Laser & Optoelectronics Progress , 2021 , 58 ( 8 ): 162 - 171 .

POIESI F , BOSCAINI D . Learning general and distinctive 3D local deep descriptors for point cloud registration [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023 , 45 ( 3 ): 3979 - 3985 .

WANG Y , SOLOMON J . Deep closest point: learning representations for point cloud registration [C ] // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway : IEEE Press , 2020 : 3522 - 3531 .

WU Z R , SONG S R , KHOSLA A , et al . 3D ShapeNets: a deep representation for volumetric shapes [C ] // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2015 : 1912 - 1920 .

FU K X , LIU S L , LUO X Y , et al . Robust point cloud registration framework based on deep graph matching [C ] // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2021 : 8889 - 8898 .

HUANG S Y , GOJCIC Z , USVYATSOV M , et al . PREDATOR: registration of 3D point clouds with low overlap [C ] // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2021 : 4265 - 4274 .

CHENG D Y , ZHAO D J , ZHANG J C , et al . PCA-based denoising algorithm for outdoor lidar point cloud data [J ] . Sensors , 2021 , 21 ( 11 ): 3703 .

王来强 , 包静 . 附加主方向判定的PCA点云数据初始配准算法 [J ] . 测绘地理信息 , 2021 , 46 ( 6 ): 59 - 62 , 66 .

WANG L Q , BAO J . Initial registration algorithm for PCA point cloud data with additional main direction decision [J ] . Journal of Geomatics , 2021 , 46 ( 6 ): 59 - 62 , 66 .

LIU J , ZHU J W , YANG J L , et al . Three-dimensional point cloud registration based on ICP algorithm employing K-D tree optimization [C ] // Proceedings of Eighth International Conference on Digital Image Processing (ICDIP 2016) . Washington : SPIE , 2016 , 10033 : 872 - 876 .

RUSU R B , BLODOW N , BEETZ M . Fast point feature histograms (FPFH) for 3D registration [C ] // Proceedings of 2009 IEEE International Conference on Robotics and Automation . Piscataway : IEEE Press , 2009 : 3212 - 3217 .

CHETVERIKOV D , SVIRKO D , STEPANOV D , et al . The trimmed iterative closest point algorithm [C ] // Proceedings of 2002 International Conference on Pattern Recognition . Piscataway : IEEE Press , 2002 : 545 - 548 .

LI J Y , HU Q W , AI M Y . Point cloud registration based on one-point RANSAC and scale-annealing biweight estimation [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2021 , 59 ( 11 ): 9716 - 9729 .

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