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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 1-7.doi: 10.6040/j.issn.1672-3961.0.2018.244

• 机器学习与数据挖掘 •    下一篇

快速四点一致性点云粗配准算法

刘世光1,2(),王海荣1,刘锦1   

  1. 1. 天津大学智能与计算学部, 天津 300350
    2. 天津市认知计算与应用重点实验室, 天津 300350
  • 收稿日期:2018-06-07 出版日期:2019-04-20 发布日期:2019-04-19
  • 作者简介:刘世光(1980—),男,山东临沂人,教授,博士,主要研究方向为可视化仿真,计算机图形学,虚拟现实等. E-mail:lsg@tju.edu.cn
  • 基金资助:
    国家自然科学基金项目(61672375);国家自然科学基金项目(61170118);天津市自然科学基金项目(14JCQNJC00100)

Fast 4-points congruent sets for coarse registration of 3D point cloud

Shiguang LIU1,2(),Hairong WANG1,Jin LIU1   

  1. 1. Division of Intelligence and Computing, Tianjin University, Tianjin 300350, Tianjin, China
    2. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin 300350, Tianjin, China
  • Received:2018-06-07 Online:2019-04-20 Published:2019-04-19
  • Supported by:
    国家自然科学基金项目(61672375);国家自然科学基金项目(61170118);天津市自然科学基金项目(14JCQNJC00100)

摘要:

为了解决四点全等集合(4-points congruent sets, 4PCS)在两片点云重叠率较低的情况下算法耗时长且配准容易失败的问题,提出快速四点一致性集合(fast 4-points congruent sets, F-4PCS)解决点云配准问题。给出一种新的选择四点基的方法,给定源点云和目标点云,分别提取出它们的边界,将边界扩展为边界特征带,在边界特征带中选取具有一致性的四点基集合,从而避免一些不必要的迭代。通过对四点基的特征限制,去除无效的四点基,减少算法的验证时间,提高计算效率。在相关数据集上的试验表明,在点云重叠率较低等情况下F-4PCS方法比4PCS方法更加高效且配准成功率较高。

关键词: 点云, 配准, 特征, 四点基, 4PCS, F-4PCS

Abstract:

In order to solve the problem that the 4-points congruent sets (4 PCS) method suffered from low computational efficiency and high registration errors when the overlap rate of two pieces of input point clouds was low, fast 4-points congruent sets (F-4PCS) was put forward. A new method for selecting four-point basis was presented. The source point cloud and target point cloud were given, their boundaries were separately extracted and extended as the boundary feature bands, and then a consistent four-point basis set was chosen from the boundary feature bands. This method could avoid some unnecessary iterations. By limiting the characteristics of the four-point basis, the invalid four-point basis was removed, it could reduce the verification time of the algorithm and improve the computational efficiency. Experiments results carried out on the relevant data sets showed that the F-4PCS method was more efficient than conventional 4PCS method in the case of low overlap rate of input point clouds and the registration success rate was higher than state-of-the-arts.

Key words: point cloud, registration, feature, 4-point basis, 4PCS, F-4PCS

中图分类号: 

  • TP391.41

图1

四点基及其表示"

图2

边界提取过程示意图"

图3

边界扩展到边界特征带示意图"

图4

PFH特征计算示意图"

图5

原始输入点云模型"

图6

本研究算法点云配准结果"

图7

Super-4PCS点云配准结果"

图8

K-4PCS、Super-4PCS和本研究算法的运行时间对比"

图9

4PCS与本研究算法的SR与RMSE的比较"

图10

不同噪声点的点云配准结果"

图11

噪声干扰下的配准结果对比"

1 伍龙华, 黄惠. 点云驱动的计算机图形学综述[J]. 计算机辅助设计与图形学学报, 2015, 27 (8): 1341- 1353.
doi: 10.3969/j.issn.1003-9775.2015.08.001
WU Longhua , HUANG Hui . Survey on points-driven computer graphics[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27 (8): 1341- 1353.
doi: 10.3969/j.issn.1003-9775.2015.08.001
2 韩宝昌, 曹俊杰, 苏志勋. 一种区域层次上的自动点云配准算法[J]. 计算机辅助设计与图形学学报, 2015, 27 (2): 313- 319.
HAN Baochang , CAO Junjie , SU Zhixun . Automatic point clouds registration based on regions[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27 (2): 313- 319.
3 孙家泽, 陈皓, 耿国华. 三维文物点云模型配准优化算法[J]. 计算机辅助设计与图形学学报, 2016, 28 (7): 1068- 1074.
doi: 10.3969/j.issn.1003-9775.2016.07.005
SUN Jiaze , CHEN Hao , GENG Guohua . Registration optimization algorithm for 3D cultural relics point clouds model[J]. Journal of Computer-Aided Design & Computer Graphics, 2016, 28 (7): 1068- 1074.
doi: 10.3969/j.issn.1003-9775.2016.07.005
4 BESL P J , MCKAY N D . A method for registration of 3-D shapes[J]. IEEE Transactions Pattern Analysis Machine Intelligence, 1992, 14 (2): 239- 256.
doi: 10.1109/34.121791
5 CHEN Y , MEDIONI G . Object modelling by registration of multiple range images[J]. Image & Vision Computing, 1992, 10 (3): 145- 155.
6 BERGEVIN R , SOUCY M , GAGNON H , et al. Towards a general multi-view registration technique[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1996, 18 (5): 540- 547.
7 BEA K H. Automated registration of unorganized point cloudsfrom terrestrial laser scanners[D]. Perth, Australia: Curtin University of Technology, 2006.
8 MINGUEZ J , MONTESANO L , LAMIRAUX F . Metric-based iterative closest point scan matching for sensor displacement estimation[J]. IEEE Transactions on Robotics, 2006, 22 (5): 1047- 1054.
doi: 10.1109/TRO.2006.878961
9 CENSI A. An ICP variant using a point-to-line metric[C]//IEEE International Conference on Robotics and Automation. Pasadena, USA: IEEE, 2008: 19-25.
10 杨玲, 谯舟三, 陈玲玲, 等. 结合Procrustes分析法和ICP算法的PICP配准算法[J]. 计算机辅助设计与图形学学报, 2017, 29 (2): 337- 343.
doi: 10.3969/j.issn.1003-9775.2017.02.016
YANG Ling , QIAO Zhousan , CHEN Lingling , et al. PICP Registration method based on procrustes analysis and ICP algorithm[J]. Journal of Computer-Aided Design & Computer Graphics, 2017, 29 (2): 337- 343.
doi: 10.3969/j.issn.1003-9775.2017.02.016
11 POTTMANN H , HUANG Q X , YANG Y L , et al. Geometry and convergence analysis of algorithms for registration of 3D shapes[J]. International Journal of Computer Vision, 2006, 67 (3): 277- 296.
12 YANG J , LI H , CAMPBELL D , et al. Go-ICP: a globally optimal solution to 3D ICP Point-Set registration[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2016, 38 (11): 2241- 2254.
13 ZHOU Q Y, PARK J, KOLTUN V. Fast global registration[C]//European Conference on Computer Vision. Springer International Publishing. Amsterdam, Netherlands: Springer, 2016: 766-782.
14 JOHNSON A. Spin-Images: a representation for 3-D surface matching[D]. Pittsburgh, USA: Carnegie Mellon University, 1997.
15 LI X, GUSKOV I. Multi-scale features for approximate alignment of point-based surfaces[C]//Eurographics Symposium on Geometry Processing. Vienna, Austria: Eurographics Association, 2005: 217.
16 CHUA C S, HAN F, HO Y K. 3D human face recognition using point signature[C]//Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, 2000. Grenoble, France: IEEE, 2000: 233-238.
17 KAICK O V , ZHANG H , HAMARNEH G , et al. A survey on shape correspondence[J]. Computer Graphics Forum, 2011, 30 (6): 1681- 1707.
doi: 10.1111/cgf.2011.30.issue-6
18 HORN B K P . Closed-form solution of absolute orientation using unit quaternions[J]. Journal of the Optical Society of America A, 1987, 5 (7): 1127- 1135.
19 IRANI S , RAGHAVAN P . Combinatorial and experimental results for randomized point matching algorithms[J]. Computational Geometry, 1999, 12 (1/2): 17- 31.
20 FISCHLER M A , BOLLES R C . Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Comm of the Acm, 1987, 24 (6): 726- 740.
21 AIGER D , MITRA N J , COHENOR D . 4-points congruent sets for robust pairwise surface registration[J]. Acm Transactions on Graphics, 2011, 27 (3): 1- 10.
22 THEILER P W , WEGNER J D , SCHINDLER K . Keypoint-based 4-points congruent sets: automated marker-less registration of laser scans[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2014, 96 (11): 149- 163.
23 THEILER P W , WEGNER J D , SCHINDLER K . Markerless point cloud registration with keypoint-based 4-points congruent sets[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, 1 (2): 283- 288.
24 MELLADEO N , AIGER D , MITRA N J . Super 4PCS fast global pointcloud registration via smart indexing[J]. Computer Graphics Forum, 2015, 33 (5): 205- 215.
25 MOHAMAD M, RAPPAPORT D, GREENSPAN M. Generalized 4-Points congruent sets for 3D registration[C]//International Conference on 3d Vision. Tokyo, Japan: IEEE, 2015: 83-90.
26 SILVA J P D J, BORGES D L, FLAVIO D B V. A dynamic approach for approximate pairwise alignment based on 4-points congruence sets of 3D points[C]//IEEE International Conference on Image Processing. Brussels, Belgium: IEEE, 2011: 889-892.
27 RUSU R B, COUSINS S. 3D is here: point cloud library (PCL)[C]//IEEE International Conference on Robotics and Automation. Shanghai, China: IEEE, 2011: 1-4.
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