基于特征点提取和PCA的改进ICP点云配准方法  

Improved ICP point cloud registration method based on feature point extraction and PCA

作　　者：马然 Ma Ran(Guangzhou Southern Surveying and Mapping Technology Co.,Ltd.,Guangzhou 510000,China)

机构地区：[1]广州南方测绘科技股份有限公司,广东广州510000

出　　处：《电子技术应用》2025年第4期110-114,共5页Application of Electronic Technique

基　　金：广东省重点领域研发计划(2023B1111050013)。

摘　　要：传统迭代最近点(Iterative Closest Point, ICP)方法进行点云配准时存在实时性差、易陷入局部极值且配准精度低等问题。提出一种基于特征点提取、主成分分析(Principal Component Analysis, PCA)粗配准和ICP精配准的三步点云配准方法。首先定义点云数据局部密度概念,并自动选择局部密度较大的点作为特征点,然后利用PCA对提取的特征点进行分析,根据PCA主分量方向计算配准所需平移和旋转参数。最后利用ICP对数据进行精配准。试验结果表明,所提方法相对于对比方法的配准精度提升超过13.4%,实时性提升超过38.2%,并且在低信噪比条件下表现出了更高的适应性,具有较高的应用前景。The traditional Iterative Closest Point(ICP)method for point cloud registration has problems such as poor real-time performance,susceptibility to local extremum,and low registration accuracy.This paper proposes a three-step point cloud registration method based on feature point extraction,Principal Component Analysis(PCA)coarse registration,and ICP fine registration.Firstly,it defines the concept of local density in point cloud data and automatically selects points with higher local density as feature points.Then,it uses PCA to analyze the extracted feature points and calculates the required translation and rotation parameters for registration based on the principal component direction of PCA.Finally,it uses ICP to perform precise data registration.The experimental results show that the proposed method improves registration accuracy by more than 13.4%compared to the comparison methods,improves real-time performance by more than 38.2%,and exhibits higher adaptability under low signal-to-noise ratio conditions,with high application prospects.

关 键 词：三维激光 点云配准 迭代最近点 局部密度 主成分分析 

分 类 号：P209[天文地球—测绘科学与技术]