Study on registration of unorganized point clouds based on inheriting and optimizing algorithm

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Study on registration of unorganized point clouds based on inheriting and optimizing algorithm

Article | 更新时间：2020-08-12

- Study on registration of unorganized point clouds based on inheriting and optimizing algorithm
- Optics and Precision Engineering Vol. 17, Issue 4, Pages: 825-831(2009)
- 作者机构：
  
  南昌大学
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN911.74
- Received：14 June 2008，
  
  Revised：08 August 2008，
  
  Published Online：25 April 2009，
  
  Published：25 April 2009
- 稿件说明：
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孟凡文. 基于继承与优化算法的无序点云拼接技术的研究[J]. 光学精密工程, 2009,17(4):825-831

Study on registration of unorganized point clouds based on inheriting and optimizing algorithm[J]. Optics and precision engineering, 2009, 17(4): 825-831.
孟凡文. 基于继承与优化算法的无序点云拼接技术的研究[J]. 光学精密工程, 2009,17(4):825-831 DOI：

Study on registration of unorganized point clouds based on inheriting and optimizing algorithm[J]. Optics and precision engineering, 2009, 17(4): 825-831. DOI：

摘要

摘要：点云拼接技术的核心是建立两个待拼接点云之间的对应关系。提出继承与优化算法进行点云精确拼接，阐述了算法原理，通过建模获取拼接过程中的旋转和平移参数，提出并分析了拼接的实现过程。采用光栅投射式三维扫描仪获取某型号汽车防雨板的6组点云数据，使用提出的算法进行点云拼接，采用多分辨率层次精度分析法对拼接结果进行误差分析，与最临近点迭代法在拼接精度、收敛速度和耗时上进行了比较。实验结果表明：继承与优化算法可实现海量无序点云的精确拼接，拼接的标准偏差小于0.10mm，两点云对拼接时间小于2秒，相比最临近点迭代法，所需迭代次数减少5次以上。

Abstract

Abstract: The key of point cloud registration is to build corresponding relation between two point clouds. Inheriting and optimizing algorithm is presented to realize precise point cloud registration. Algorithm principle is explained

and rotating parameters and shifting parameters can be obtained by building model. Realizing procedure of the algorithm is brought forward and analyzed. 6 groups of point clouds of car flashing are acquired by grating projection 3D scanning instrument

and point clouds registration based on presented algorithm is carried out. Registration error is analyzed by multi resolution hiberarchy method. It indicates that the proposed algorithm can realize precise registration of unorganized point clouds. Registration standard error is less than 0.10mm

and the registration time of 2 point clouds is less than 2 seconds. Compared to ICP algorithm

the required iterative number is reduced by more than 5 times.

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