电子科技2024，Vol.37Issue(7)：60-65,6.DOI:10.16180/j.cnki.issn1007-7820.2024.07.008
面向盾牌形貌重建的双层采样点云粗配准方法
Coarse Registration Method of Double-Layer Sampling Point Cloud for Shield Topography Reconstruction
摘要
Abstract
In view of the problems of low precision and time-consuming point cloud coarse registration in the process of 3D reconstruction of shield surface topography,this study proposes a double-layer sampling point cloud coarse registration algorithm based on the traditional RANSAC(Random Sample Consensus)point cloud coarse regis-tration algorithm framework.In each iteration of the first layer of the proposed algorithm,the single-point sampling is used for rigid constraints to reduce the size of the corresponding set,which is regarded as the"interior point candi-date set"of the second layer.The second layer of the algorithm performs continuous random sampling of two points and computes their respective minimum models.The optimal rigid body transformation matrix is obtained by iterative-ly maximizing the consistent set and using the least square method.In the experimental stage,6 groups of shield point clouds with different degrees of down sampling are used,and the RANSAC algorithm and the proposed algorithm are used to conduct a point cloud coarse registration comparison experiment.Experimental results show that the proposed algorithm is superior to the RANSAC algorithm in both rough registration speed and rough registration accuracy.The registration speed is about twice that of the RANSAC algorithm,and the root-mean-square error of the proposed al-gorithm is 10-3 mm,which is nearly 400 times higher than the RANSAC algorithm.关键词
形貌重建/粗配准/RANSAC/双层采样/单点采样/刚性约束/一致性集合/盾牌Key words
topography reconstruction/rough registration/RANSAC/double-layer sampling/single point sampling/rigid constraint/consensus set/shield分类
信息技术与安全科学引用本文复制引用
万晟睿,周志峰,吴明晖,王立端,周围..面向盾牌形貌重建的双层采样点云粗配准方法[J].电子科技,2024,37(7):60-65,6.基金项目
上海市优秀学术/技术带头人计划(22XD1433500)Shanghai Excellent Academic/Technical Leader Program(22XD1433500) （22XD1433500）
