Research On Fine Matching Method Of Multi-resolution Measurement Point Cloud Data

The three-dimensional optical measurement technology is an important means for realizing the digitization of entities.Due to the object's self-occlusion,scanning range limitation,and scanning principle,a single-scan of a three-dimensional optical measurement system can only obtain a point cloud on one side of the object,in order to realize large volume,The multi-surface reconstruction of three-dimensional solids must be scanned from different perspectives and a complete entity digital model must be constructed through matching techniques.Therefore,the development of point cloud matching technology has important theoretical and practical value.In this paper,the multi-viewpoint cloud matching problem in 3D optical measurement is studied.A new multi-resolution measurement point cloud matching method based on tower decomposition is proposed and implemented.Compared with the existing point cloud matching method,the new method has greatly improved the matching error and matching efficiency.The main research contents and achievements are as follows:(1)The point cloud pretreatment technology was studied.The KD tree model of 3D point cloud is established,and the nearest point search algorithm based on KD tree is implemented to improve the neighboring search efficiency of point data and the corresponding point search efficiency.The point vector normal vector estimation method is studied and the point cloud is implemented.Triangular normal vector estimation and normal vector estimation based on local surface fitting.(2)The method of transforming three-dimensional point cloud into two-dimensional grayscale image is studied,and a grayscale transformation method based on K-neighborhood search algorithm and Hotelling transform is implemented.The method can accurately use the local feature information of the point cloud to construct a local coordinate system of a point data,and convert the coordinates of the point data to this coordinate system,and finally use all converted Z-axis coordinate information and the original point cloud.The grid line information builds a corresponding two-dimensional grayscale map.(3)Studied the tower decomposition algorithm of the image,realized Gaussian pyramid decomposition and Laplacian pyramid decomposition algorithm of the image,and decomposed the grayscale image after transformation of the three-dimensional point cloud to construct the Gaussian pyramid and Laplac Pyramids.(4)A new multi-resolution measurement point cloud matching algorithm is proposed.First,the point cloud is converted into a two-dimensional grayscale image,and the grayscale image is subjected to tower decomposition.Select the appropriate pyramid layer to pick up the two-dimensional seed points,map them to the three-dimensional point cloud,and compose the three-dimensional seed point set.Finally,use the picked seed points for ICP matching to establish a complete point cloud model.Based on this theory,this algorithm was implemented on Visual Studio platform using C++ and Open GL open graphics library,and compared with the existing point cloud matching algorithm.The experimental results show that the new method is both in terms of matching efficiency and matching accuracy.There is a clear increase.