中图分类号：P209 文献标志码：A DOI: 10.16157/j.issn.0258-7998.245473
中文引用格式： 马然. 基于特征点提取和PCA的改进ICP点云配准方法[J]. 电子技术应用，2025，51(4)：110-115.
英文引用格式： Ma Ran. Improved ICP point cloud registration method based on feature point extraction and PCA[J]. Application of Electronic Technique，2025，51(4)：110-115.

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

Abstract： 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.

Key words : 3D laser；point cloud registration；iteration closest point；local density；principal component analysis