Interpolation Of Point Clouds Data Based On Kriging Interpolation

3D laser scanning technology is also known as “high resolution measurements (HighDefinition Surveying, HDS)”. It was a new measurement technique rapidly developed in themiddle1990s and has been widely applied in topographic survey, protection of ancientbuildings and reverse engineering, etc.We can obtain a large area high precision3D coordinate data in surveying area by using3D laser scanner, then construct the high precision digital elevation model (DEM).Theacquisition of terrain point cloud data will be influenced by external environment, such as thelarge number of non ground points on the surface (small shrubs and weeds, etc.).The relatednon ground points need to be removed in the post processing of point cloud data. The actualacquisition terrain point cloud data distributes irregularly. If we want to use them to constructthe DEM, we must repair these irregular and uneven point cloud datas. Spatial interpolationtechnology is one of the effective ways to realize the point cloud data repairing. Commoninterpolation methods contain kriging interpolation, inverse distance weighted interpolation,nearest neighbor interpolation, natural neighbor interpolation and so on. The paper mainlystudies the repair of point cloud data using Kriging interpolation algorithm.The paper did a systematical research on the basis of3D laser scanning technology,Krikin interpolation algorithm principle and point cloud data process of Krikin interpolation.The main research contents are as follows:(1)Introduced the development process of3D laser scanner technology, the relatedresearch situation, the composition, principle, working process and main application field.(2) Introduced the basic principle of Kriging interpolation, the relevant theories ofKriging interpolation algorithm in detail, such as the set of regional research variables,experimental variogram values, theoretical variogram model, experimental variogram fittingfunction value, and theoretical variogram model etc.(3) used the Kriging interpolation, inverse distance weighted interpolation, nearestneighbor interpolation for spatial interpolation to the point cloud data, and compared thesethree kinds of interpolation results with the original experimental data, compared theadvantages and disadvantages of Kriging interpolation with other interpolation algorithms. Did a research about influencing factors of Kriging interpolation results at the same time.