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• 计算机图形学 • 上一篇    下一篇

基于局部特征的点云配准算法

  

  1. 1. 咸阳师范学院教育科学学院，陕西咸阳 712000；
    2. 西北大学信息科学与技术学院，陕西西安 710127；
    3. 北京师范大学信息科学与技术学院，北京 100875
  • 出版日期:2018-06-30 发布日期:2018-07-10
  • 基金资助:
    国家自然科学基金项目(61731015)；咸阳发展研究院服务地方经济社会发展项目(2018XFY007)

Point Cloud Registration Algorithm Based on Local Features

  1. 1. School of Education Science, Xianyang Normal University, Xianyang Shaanxi 712000, China;
    2. School of Information Science and Technology, Northwest University, Xi’an Shaanxi 710127, China;
    3. School of Information Science and Technology, Beijing Normal University, Beijing 100875, China
  • Online:2018-06-30 Published:2018-07-10

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摘要： 针对覆盖率较低的点云，提出一种基于局部特征的点云配准算法。首先提取点云
的局部深度、法线偏角和点云密度等局部特征，得到局部特征描述子；然后计算局部特征集的
相关性，得到相关候选点集；再次通过删减外点达到点云粗配准的目的；最后采用基于旋转角
约束和动态迭代系数的改进迭代最近点(ICP)算法，实现点云的细配准。实验结果表明，基于局
部特征的点云配准算法可以实现覆盖率较低点云的精确配准，是一种精度高、速度快的点云配
准算法。

关键词: 点云配准, 局部特征, 迭代最近点, 旋转角约束, 动态迭代系数

Abstract: Aiming at low-coverage-rate point clouds, a registration algorithm was proposed based on
local features in the paper. Firstly, local features including the local depth, deviation angle between
normals and point cloud density are extracted, and the local feature descriptor is obtained. Secondly,
the correspondence of local feature sets is calculated and the corresponding candidates are gained.
Thirdly, the outliers are eliminated and coarse registration is achieved. Lastly, an improved iterative
closest point (ICP) algorithm based on the rotation angle constraint, and the dynamic iterative
coefficient is employed to complete fine point cloud registration. The experiment results reveal that
the point cloud registration algorithm could achieve the precise registration of low-coverage-rate
point cloud, based on local features, a high-precision and fast one.

Key words: point cloud registration, local feature, iterative closest point, rotation angle constraint;
dynamic iterative coefficient