Research On ICP Point Cloud Registration Algorithm Based On Local Geometry Information

With the rapid development of high-precision measurement sensors,3D point clouds are widely used to depict the world.As an important part of 3D point cloud processing,point cloud registration is a key technology to complete practical projects such as 3D reconstruction,virtual reality,cultural relic restoration,and intelligent transportation.The ICP algorithm is the most widely used point cloud registration algorithm.In order to solve the limitations of the algorithm itself and improve the accuracy and efficiency of point cloud registration,this paper focuses on extracting feature points,calculating descriptors,obtaining matching point pairs and accurate four aspects of registration are studied,and two point cloud registration algorithms are proposed:In order to solve the problems of poor robustness,low registration accuracy and efficiency of ICP algorithm in the case of noise interference and data loss,an improved ICP point cloud registration algorithm based on FPFH is proposed.First,extract point cloud features by fusing internal morphological descriptors and normal vector angle changes;secondly,use exponential function to improve Euclidean distance as the weight coefficient of FPFH algorithm,and use it to describe feature points;then use double constraints and unit four the arity algorithm completes the initial registration;finally,a bidirectional k-dimensional tree is constructed for the ICP algorithm,and the weight of each point pair is calculated by the distance as the weighting formula of the ICP iterative error function.The experimental data set uses the Stanford model and two sets of actual point clouds.The experimental results show that the algorithm solves the limitations of the ICP algorithm in the environment of noise interference and data loss.Compared with other algorithms,the registration accuracy of physical point clouds is improved by at least 11%In order to solve the problem of low registration accuracy and registration efficiency of the ICP algorithm when registering partially overlapping object point clouds,a point cloud registration algorithm based on the description and matching of neighborhood point information is proposed.First,the feature points are extracted according to the curvature change,measurement angle and eigenvalue properties of the points under three radius ratios;secondly,the improved normal vector angle,point density and curvature value are calculated to obtain the multi-scale matrix descriptor;then,for the descriptor establishes a k-dimensional tree to obtain the matching relationship,and proposes a combination of geometric feature constraints and rigid distance constraints to eliminate wrong point pairs to achieve rough registration;finally,the k-dimensional tree is used to improve the ICP algorithm to complete accurate registration.The research designed two sets of experiments of real object point cloud registration and Stanford model simulated real object registration.The experimental results show that,compared with other algorithms,the registration accuracy and efficiency of the algorithm in the actual partial overlapping point cloud registration are improved by at least 29% and 40%;in the Stanford simulation experiment,the registration accuracy and efficiency of the algorithm are improved by at least 11% and 12%.There are 55 figures,9 tables and 69 references in this paper.