Research On 3D Point Cloud Registration And Plane Extractio

In the digital age,using 3D scanning technology to obtain information such as the spatial position and attributes of objects and scenes has become one of the important methods of information collection.The collected 3D point cloud data occupies an important position in the entire data category and is widely used in various fields.In3 D point cloud data processing,point cloud registration is the basis of data processing,and the effect of registration will directly affect the accuracy of subsequent data processing;plane extraction can obtain the plane information in the scene,and reflect the specific information of the scene from a small range of planes.Point cloud registration and plane extraction is a continuous data processing process.How to obtain better registration and plane extraction results is one of the important contents of 3D point cloud research.Therefore,this paper conducts 3D point cloud registration and plane extraction research,the main contents are as follows:(1)Aiming at the problem that it is difficult to have both efficiency and accuracy in point cloud registration,an improved point cloud registration method combining morphological description and histogram features is proposed.This method combines intrinsic shape signatures feature points,binary signatures of the histograms of orientations feature descriptors,and Hamming distance to complete the initial registration,and then uses the improved ICP registration method to complete the final precise registration.Based on two sets of public data,five typical registration methods were used to compare with the proposed method.The results show that the proposed registration method has better registration accuracy and time than the other methods.Especially in terms of registration accuracy,compared with the ISS+SHOT+SAC-IA method,which is the best registration accuracy in the comparison method,the accuracy of the method in this paper has increased by 38.2% and 80.3% in the two sets of data.It shows that the point cloud registration method proposed in this paper has better registration efficiency and registration accuracy.(2)In order to avoid the problems of low extraction efficiency and unguaranteed accuracy in the plane extraction process,a point cloud plane extraction method combining super voxel segmentation and random sampling consistency is proposed.The method uses the result of super voxel segmentation to constrain the selection range of initial random points in the process of plane extraction,and removes the extracted plane points through circular operation to realize multiplane extraction.Based on three sets of scene data,the traditional method is compared with the method in this paper.The results show that the plane extraction method in this paper is not inferior to the traditional method in terms of the effectiveness and accuracy of plane extraction.In addition,the method in this paper has a great advantage in extraction time.Compared with the traditional method,the extraction time of the three sets of data has increased by 97.10%,89.13% and 88.30%.It shows that the point cloud plane extraction method proposed in this paper has better extraction effect and higher extraction efficiency.(3)Applicability analysis on the measured data.In order to test the applicability of the method proposed in this paper on the measured data.Based on the measured single house data and multiple house data,experiments are carried out using the proposed method.The results show that the proposed method is more applicable to the data of a single house than to the data of multiple houses within the experiment.However,from the overall results of the two sets of measured data,the improved method proposed in this paper has better registration and plane extraction effects in some actual environment,which proves that the research method of point cloud registration and plane extraction in this paper has certain practical significance.