北京测绘2025，Vol.39Issue(1)：33-39,7.DOI:10.19580/j.cnki.1007-3000.2025.01.006
曲率特征约束的多视点云CPD配准方法
CPD registration method of multi-view point clouds based on curvature feature constraints
张银屏 1董明 1毛力妹1
作者信息
- 1. 江西省赣核测绘地理信息有限公司,江西 上饶 334000
- 折叠
摘要
Abstract
The probabilistic model method is not suitable for point cloud registration in large-scale scenarios.To address this issue,this paper proposed a matching method based on local principal curvature feature constraints of point clouds.By matching the feature quaternions with point cloud down-sampling,the paper calculated the nearest neighbor centroids of the corresponding feature in the original point cloud and used a coherent point drift algorithm for the point sets.The experiments on the Stanford dataset show that the proposed method has faster speed and higher accuracy in small-scale point cloud registration compared to existing methods,and it has lower requirements for the initial pose of point clouds.The proposed method obtains root-mean-square error(RMSE)of 6.073×10-3 mm on Bunny data when the initial pose difference is small.When the initial pose is poor,the iterative closest point(ICP)algorithm is ineffective,and the proposed method obtains RMSE of 3.743×10-1 mm and 1.639 mm on Dragon data.The experiments on the WHU-TLS dataset show that the proposed algorithm can provide fine initial values for the ICP algorithm automatically and can be used for point cloud registration in large-scale scenarios.关键词
点云配准/连贯点漂移/局部主曲率/点云下采样Key words
point cloud registration/coherent point drift/local principal curvature/point cloud down-sampling分类
天文与地球科学引用本文复制引用
张银屏,董明,毛力妹..曲率特征约束的多视点云CPD配准方法[J].北京测绘,2025,39(1):33-39,7.
