计算机与数字工程2025，Vol.53Issue(4)：1187-1193,7.DOI:10.3969/j.issn.1672-9722.2025.04.045
基于隧道截面点云数据的椭圆拟合算法的改进
Improvement of Ellipse Fitting Algorithm Based on Tunnel Section Point Cloud Data
吴宇迪 1肖颖超 1徐永安1
作者信息
- 1. 扬州大学信息工程学院 扬州 225127
- 折叠
摘要
Abstract
Because the traditional ellipse fitting algorithm is easy to be affected by noise points,the fitting effect is not good.In order to improve the uncertainty and fitting accuracy of ellipse detection,this paper proposes an ellipse fitting method which com-bines the shield data storage structure with point cloud denoising.Firstly,the tunnel cross section point cloud data is stored in the shield data structure and preprocessed in this methods.After the processed data points are obtained,a new point cloud denoising method is used to remove a certain amount of noise.At the same time,the optimal data set is obtained and the final elliptic model is fitted.Theoretical analysis and actual image fitting results show that the proposed method can better remove noise points,and has better fitting results and higher stability than the traditional ellipse fitting method.关键词
椭圆拟合/盾构数据结构/点云去噪/迭代Key words
ellipse fitting/shield data structure/point cloud denoising/iteration分类
信息技术与安全科学引用本文复制引用
吴宇迪,肖颖超,徐永安..基于隧道截面点云数据的椭圆拟合算法的改进[J].计算机与数字工程,2025,53(4):1187-1193,7.
