Point cloud registration based on improved 3DSC

ZHAO Yun-tao; QI Jia-xiang; LI Wei-gang; GAN Lei

doi:10.37188/CJLCD.2022-0156

您当前的位置：

首页 >

文章列表页 >

Point cloud registration based on improved 3DSC

Image Processing | 更新时间：2022-11-22

- Point cloud registration based on improved 3DSC
- Chinese Journal of Liquid Crystals and Displays Vol. 37, Issue 12, Pages: 1590-1597(2022)
- 作者机构：
  
  1.武汉科技大学冶金自动化与检测技术教育部工程研究中心，湖北武汉 430081
  2.武汉科技大学信息科学与工程学院，湖北武汉 430081
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51774219)
- DOI：10.37188/CJLCD.2022-0156
  CLC：
扫描看全文
ZHAO Yun-tao, QI Jia-xiang, LI Wei-gang, et al. Point cloud registration based on improved 3DSC. [J]. Chinese Journal of Liquid Crystals and Displays 37(12):1590-1597(2022)
DOI：

ZHAO Yun-tao, QI Jia-xiang, LI Wei-gang, et al. Point cloud registration based on improved 3DSC. [J]. Chinese Journal of Liquid Crystals and Displays 37(12):1590-1597(2022) DOI： 10.37188/CJLCD.2022-0156.

摘要

针对目前常用配准算法不能满足生产制造行业中高精度工艺要求的问题，本文基于三维点云提出一种改进三维形状上下文（3DSC）点云配准的有效解决方案。首先，通过改进的降采样方式设定阈值采集轮廓点云，对采集的点云依次进行三维网格划分形成形状上下文。然后，进行改进的3DSC初始配准，进而采用迭代最近点（ICP）精确配准，实现了源点云与目标点云之间的旋转平移变换。为验证改进算法的有效性，采用FPFH-ICP、PFH-ICP、传统3DSC和本文改进算法进行配准实验对比。实验结果表明，对于bunny点云和flowerpot点云，本文改进算法精度分别可达2.253 55e-05 m和9.969 02e-06 m，明显优于其他算法的配准精度。与传统3DSC配准算法相比，改进的3DSC配准算法可节省75%~85%的配准时间。改进的3DSC点云配准方法有利于提高配准精度且能优化配准时间，提高了配准效率。

Abstract

In view of the problem that the commonly used registration algorithms cannot meet the high-precision process requirements in the manufacturing industry， this paper proposes an improved 3DSC point cloud registration based on the 3D point cloud. Firstly， the threshold value is set to collect the contour point cloud by the improved down-sampling method， and the collected point cloud is divided into 3D mesh in turn to form a 3D shape context. Then， the improved 3DSC coarse registration is performed， and the ICP fine registration is used to realize the rotation and translation transformation between the source point cloud and the target point cloud. In order to verify the effectiveness of the improved algorithm， the traditional 3DSC， FPFH-ICP， PFH-ICP and the improved algorithm in this paper are used to compare the registration experiments. The experiment results show that the accuracy of the improved algorithm can reach 2.253 55e-05 m and 9.969 02e-06 m respectively for the bunny point cloud and the flowerpot point cloud， which is obviously better than the registration accuracy of other algorithms. Compared with the traditional 3DSC registration algorithm， the improved 3DSC registration algorithm can save 75%~85% of the registration time. The improved 3DSC point cloud registration method is beneficial to improve the registration accuracy， optimize the registration time， and improve the registration efficiency.

关键词

点云配准三维形状上下文迭代最近点三维视觉

Keywords

point cloud registration3D shape contextiterative closest point3D vision

references

LI P， WANG R S， WANG Y X， et al. Evaluation of the ICP algorithm in 3D point cloud registration ［J］. IEEE Access， 2020， 8： 68030-68048. doi: 10.1109/access.2020.2986470http://dx.doi.org/10.1109/access.2020.2986470

汤慧，周明全，耿国华.基于扩展的点特征直方图特征的点云匹配算法［J］．激光与光电子学进展， 2019，56（24）：241503. doi: 10.3788/lop56.241503http://dx.doi.org/10.3788/lop56.241503

TANG H， ZHOU M Q， GENG G H. Point cloud registration algorithm based on extended point feature histogram feature ［J］. Laser & Optoelectronics Progress， 2019， 56（24）： 241503 （in Chinese）. doi: 10.3788/lop56.241503http://dx.doi.org/10.3788/lop56.241503

LU J， GUO C L， FANG Y， et al. Fast point cloud registration algorithm using multiscale angle features ［J］. Journal of Electronic Imaging， 2017， 26（3）： 033019. doi: 10.1117/1.jei.26.3.033019http://dx.doi.org/10.1117/1.jei.26.3.033019

BELONGIE S， MALIK J， PUZICHA J. Shape matching and object recognition using shape contexts ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2002， 24（4）： 509-522. doi: 10.1109/34.993558http://dx.doi.org/10.1109/34.993558

KURNIANGGORO L， WAHYONO， JO K H. A survey of 2D shape representation： methods， evaluations， and future research directions ［J］. Neurocomputing， 2018， 300： 1-16. doi: 10.1016/j.neucom.2018.02.093http://dx.doi.org/10.1016/j.neucom.2018.02.093

KÖRTGEN M， PARK G J， NOVOTNI M， et al. 3D shape matching with 3D shape contexts ［C］//The 7th Central European Seminar on Computer Graphics. Slovakia： Budmerice， 2003： 5-17.

KOKKINOS I， BRONSTEIN M M， LITMAN R， et al. Intrinsic shape context descriptors for deformable shapes ［C］//2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence： IEEE， 2012： 159-166. doi: 10.1109/cvpr.2012.6247671http://dx.doi.org/10.1109/cvpr.2012.6247671

LI Z M， KUANG Z Z， LIU Y J， et al. Multiscale shape context and re-ranking for deformable shape retrieval ［J］. Computers & Graphics， 2016， 54： 8-17. doi: 10.1016/j.cag.2015.07.002http://dx.doi.org/10.1016/j.cag.2015.07.002

LIU W H， LI Z H， ZHANG G， et al. Adaptive 3D shape context representation for motion trajectory classification ［J］. Multimedia Tools and Applications， 2017， 76（14）： 15413-15434. doi: 10.1007/s11042-016-3841-0http://dx.doi.org/10.1007/s11042-016-3841-0

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. doi: 10.3390/app11083426http://dx.doi.org/10.3390/app11083426

卜琰，朱世强，王志，等.基于改进形状上下文特征的工件识别与定位［J］.机械工程与自动化，2016（6）：40-41，44. doi: 10.3969/j.issn.1672-6413.2016.06.016http://dx.doi.org/10.3969/j.issn.1672-6413.2016.06.016

BU Y， ZHU S Q， WANG Z， et al. Workpiece recognition and localization based on improved shape context features ［J］. Mechanical Engineering & Automation， 2016（6）： 40-41， 44. （in Chinese）. doi: 10.3969/j.issn.1672-6413.2016.06.016http://dx.doi.org/10.3969/j.issn.1672-6413.2016.06.016

訾玲玲，丛鑫，杨培.基于部件上下文关系的三维形状功能识别［J］.计算机应用研究，2019，36（1）：296-298. doi: 10.19734/j.issn.1001-3695.2017.08.0898http://dx.doi.org/10.19734/j.issn.1001-3695.2017.08.0898

ZI L L， CONG X， YANG P. 3D shape function recognition based on contextual relationship of shape parts ［J］. Application Research of Computers， 2019， 36（1）： 296-298. （in Chinese）. doi: 10.19734/j.issn.1001-3695.2017.08.0898http://dx.doi.org/10.19734/j.issn.1001-3695.2017.08.0898

ZHU C， YANG J Y， SHAO Z P， et al. Vision based hand gesture recognition using 3D shape context ［J］. IEEE/CAA Journal of Automatica Sinica， 2021， 8（9）： 1600-1613. doi: 10.1109/jas.2019.1911534http://dx.doi.org/10.1109/jas.2019.1911534

LIU L L， LIU B L. Comparison of several different registration algorithms ［J］. International Journal of Advanced Network， Monitoring and Controls， 2020， 5（1）： 22-27. doi: 10.21307/ijanmc-2020-004http://dx.doi.org/10.21307/ijanmc-2020-004

侯彬，金尚忠，王赟，等.点云配准方法在粗配准中的比较［J］.激光与光电子学进展，2020，57（8）：081502. doi: 10.3788/LOP57.081502http://dx.doi.org/10.3788/LOP57.081502

HOU B， JIN S Z， WANG Y， et al. Comparison of point cloud registration methods in coarse registration ［J］. Laser & Optoelectronics Progress， 2020， 57（8）： 081502. （in Chinese）. doi: 10.3788/LOP57.081502http://dx.doi.org/10.3788/LOP57.081502

MORI G， BELONGIE S， MALIK J. Efficient shape matching using shape contexts ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2005， 27（11）： 1832-1837. doi: 10.1109/tpami.2005.220http://dx.doi.org/10.1109/tpami.2005.220

BELONGIE S， MORI G， MALIK J. Matching with shape contexts ［M］//KRIM H， YEZZI A. Statistics and Analysis of Shapes. Boston： Birkhäuser， 2006： 81-105. doi: 10.1007/0-8176-4481-4_4http://dx.doi.org/10.1007/0-8176-4481-4_4

TOMBARI F， SALTI S， DI STEFANO L. Unique shape context for 3D data description ［C］//Proceedings of the ACM Workshop on 3D Object Retrieval. Firenze： ACM， 2010： 57-62. doi: 10.1145/1877808.1877821http://dx.doi.org/10.1145/1877808.1877821

毕勇，潘鸣奇，张硕，等.三维点云数据超分辨率技术［J］.中国光学，2022，15（2）：210-223. doi: 10.37188/CO.2021-0176http://dx.doi.org/10.37188/CO.2021-0176

BI Y， PAN M Q， ZHANG S， et al. Overview of 3D point cloud super-resolution technology ［J］. Chinese Optics， 2022， 15（2）： 210-223. （in Chinese）. doi: 10.37188/CO.2021-0176http://dx.doi.org/10.37188/CO.2021-0176

孙兴龙，韩广良，郭立红，等.采用轮廓特征匹配的红外-可见光视频自动配准［J］.光学精密工程，2020，28（5）：1140-1151.

SUN X L， HAN G L， GUO L H， et al. Infrared-visible video automatic registration with contour feature matching ［J］. Optics and Precision Engineering， 2020， 28（5）： 1140-1151. （in Chinese）

刘长吉，郝志成，杨锦程，等.基于实例分割的目标三维位置估计方法［J］.液晶与显示，2021，36（11）：1535-1544. doi: 10.37188/CJLCD.2021-0069http://dx.doi.org/10.37188/CJLCD.2021-0069

LIU C J， HAO Z C， YANG J C， et al. Object 3D position estimation based on instance segmentation ［J］. Chinese Journal of Liquid Crystals and Displays， 2021， 36（11）： 1535-1544. （in Chinese）. doi: 10.37188/CJLCD.2021-0069http://dx.doi.org/10.37188/CJLCD.2021-0069

Views

140

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Real-time high-precision model reconstruction based on global optimization

Point cloud registration method based on electro-magnetic tracking system

A key point detection method of 3 D face based on multi-feature

Point cloud registration method based on reliable optimal transport

Related Author

No data

Related Institution

Smart Computational Imaging Laboratory （SCILab）， School of Electronic and Optical Engineering， Nanjing University of Science and Technology

Smart Computational Imaging Research Institute （SCIRI） of Nanjing University of Science and Technology

Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense

College of Computer Science and Technology, Shandong University of Technology

School of Resources & Surveying Engineering, Shaanxi Energy Institute

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰