The Application Of Registration Of ICP Algorithm Base On Adaptive Kriging

In multi-domain engineering applications such as 3D printing,reverse engineering and virtual reality,multi-view point cloud alignment is the basis for the rapid and accurate reconstruction of curved surfaces of 3D models.Meanwhile,its method is also one of the research hotspots of computer vision technology.In recent years,the research of point cloud alignment algorithm has become more and more mature.In particular,the Iterative Closest Point(ICP)point cloud alignment algorithm and its variated algorithm have good robustness and alignment accuracy in solving alignment problem of small-angle staggered point clouds,which is one of the most widely used methods of point cloud alignment.Nevertheless,this kind of point cloud alignment algorithm is often affected by the initialization of the point cloud,and the alignment accuracy is easily trapped in the local optimum,thus failing to achieve accurate alignment of multiple view clouds.In this study,the optimization problem of initial position involved in ICP point cloud alignment algorithm is explored and discussed and an ICP point cloud alignment algorithm based on adaptive Kriging model is proposed by categorizing different point cloud alignment methods.The main contents and innovations are as follows:1)The principle and implementation flow of the traditional ICP point cloud alignment algorithm are elaborated and the initial transformation matrix for solving accurate alignment is proposed,which is the key link to realize accurate alignment of multi-view cloud.Aiming at the nonconvex optimization problem of solving the initial transformation matrix,the corresponding simulation model and feasible region are proposed.In addition,simulated annealing algorithm is used to optimize the simulation model and the transformation matrix of optimization is solved to realize the point cloud optimal alignment of ICP algorithm.2)Aiming at the problem of high time cost caused by simulation models in the process of simulated annealing algorithm optimization,Kriging response surface is used to fit and replace the simulation model in the optimization process of the simulated annealing algorithm to improve the efficiency of the point cloud optimal alignment of ICP algorithm.Based on this,the present study introduces the experimental design method,the construction principle of Kriging response surface and its parameter solving problem in detail.The test case is used to verify the good rubustness and efficiency of the point cloud alignment of ICP algorithm based on Kriging response surface.3)In order to further improve the stability of Kriging response surface model reconstruction,adaptive Kriging response surface based on adaptive sampling is proposed.By designing candidate sample sets and using the criteria of irrelevance,space filling and Kriging response surface prediction error,candidate sample points that need to be adaptively increased are dynamically determined,and Kriging response surface is adaptive constructed.The test case is used to verify the better flexible of ICP point cloud alignment algorithm based on adaptive Kriging response surface.4)Based on the test case that ICP point cloud alignment algorithm for adaptive Kriging response surface is applied to ideal point cloud and multi-view scanning of the Stanford Rabbit Engineering Case Experiment,the present study verifies that the algorithm involved in the research has good alignment accuracy and stability for different initial positions and point cloud alignment problems with Gaussian noise by comparing with the traditional ICP point cloud alignment algorithm and point cloud alignment algorithm based on normal vector features.This study also analyzes and discusses the surface reconstruction accuracy of multiview cloud alignment results in engineering cases,further proving that the algorithm has certain engineering application value.