顾及几何特征信息分类中心关键点的改进ICP点云配准
Improved ICP point cloud registration considering the key points of geometric feature information classification
张浩然 1陈国平 2赵辉友 1赵俊三2
作者信息
- 1. 昆明理工大学国土资源工程学院,昆明 650031
- 2. 昆明理工大学国土资源工程学院,昆明 650031;智慧矿山地理空间信息集成创新重点实验室,昆明 650093;云南省高校自然资源空间信息集成与应用科技创新团队,昆明 650211
- 折叠
摘要
针对传统配准方法效率低、收敛慢和精度低的问题,提出了一种顾及多几何特征信息分类中心关键点的改进ICP 点云配准方法.首先点云进行体素格网下采样后,去除地面点,加快配准效率;后采用协方差求解特征值进行几何特征信息分析非地面点云,再使用欧式距离法聚类.将聚类的中心点提取出作为关键点,通过FPFH算法对特征关键点进行描述,使同名特征中心关键点完成正确配对,得到初始变换估计矩阵;最后采用双向KD-tree和最近邻距离比改进的点到面ICP 算法进行精确配准,并引入Tukey损失函数抵抗离群噪声.与4 种方法进行比较,结果表明本算法的RMSE为 0.202 6 m,耗时19.426 s,配准精度及效率更高.
Abstract
In order to solve the problems of low efficiency,slow convergence and low accuracy of traditional regis-tration methods,an improved ICP point cloud registration method considering the key points of multi-geometric feature information classification center was proposed.Firstly,the point clouds are downsampled by the voxel grid,and the ground points are removed to speed up the registration efficiency,and then the covariance is used to solve the eigenval-ues for the geometric feature information analysis of the non-ground point clouds,and then the Euclidean distance method is used for clustering.The center point of the cluster was extracted as the key point,and the feature key points were described by the FPFH algorithm,so that the center key points of the feature with the same name were correctly paired,and the initial transformation estimation matrix was obtained.Finally,the bidirectional KD-tree and point-to-area ICP algorithm with improved nearest neighbor distance ratio are used for accurate registration,and the Tukey loss function is introduced to resist outlier noise.Compared with the four methods,the results show that the RMSE of the proposed algorithm is 0.202 6 m,which takes 19.426 seconds,and the registration accuracy and efficiency are higher.
关键词
点云配准/几何特征信息/欧式距离聚类/中心关键点/改进ICP点到面/三维重建Key words
point cloud registration/geometric feature information/euclidean distance clustering/central key points/improved ICP point-to-surface/3D reconstruc-tion引用本文复制引用
出版年
2024
NETL
NSTL
万方数据