自动化与信息工程2024，Vol.45Issue(2)：14-21,8.DOI:10.3969/j.issn.1674-2605.2024.02.003
基于全局特征地图的由粗到细点云配准算法
Coarse-to-fine Point Cloud Registration Algorithm Based on Global Feature Maps
摘要
Abstract
Aiming at the problems of low accuracy,poor robustness,and poor universality of existing point cloud registration algorithms on non-repetitive scanning LiDAR,a coarse-to-fine point cloud registration algorithm based on global feature map(CTF-ICP)is proposed,and a non-repetitive scanning LiDAR odometer is implemented.This algorithm uses Gaussian distribution to characterize the local point cloud distribution and construct a global feature map.The registration stage includes coarse registration and fine registration.Firstly,using normal distribution transformation to achieve frame to frame coarse registration between continuous point cloud frames;Then,based on the coarse registration results,the current point cloud is mapped to the global feature map,and the eigenvalues of the global feature covariance matrix at the corresponding positions are normalized to achieve precise frame to map registration;Finally,a comparative experiment will be conducted between the algorithm proposed in this article and other commonly used registration algorithms.The experimental results show that the algorithm proposed in this paper can adapt well to non-repetitive scanning LiDAR,and the registration accuracy and speed are significantly improved compared to commonly used registration algorithms;Meanwhile,ablation experiments have demonstrated the effectiveness of the coarse-to-fine point cloud registration algorithm and the global feature map.关键词
全局特征地图/由粗到细点云配准算法/非重复扫描式激光雷达里程计/高斯分布/正态分布变换Key words
global feature map/coarse-to-fine point cloud registration/non-repetitive LiDAR odometry/Gaussian distribution/normal distribution transformation分类
信息技术与安全科学引用本文复制引用
李文,林旭滨..基于全局特征地图的由粗到细点云配准算法[J].自动化与信息工程,2024,45(2):14-21,8.基金项目
广东省基础与应用基础研究基金(2022A1515140044) （2022A1515140044）
