面向盾牌形貌重建的双层采样点云粗配准方法OA

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.