Research On 3D Pipeline Point Cloud Segmentation And Recognition Technology

In recent years,with the development of 3D modeling technology,3D models have gradually replaced 2D drawings as the main reference materials for processing factory reconstruction and expansion.Laser scanning technology is one of the main ways to reconstruct the 3D model of processing factory.The 3D point cloud model of processing factory is obtained by laser scanning technology,and the 3D model is reconstructed by using related point cloud processing technology.Compared with the traditional 2D drawings,the 3D model can observe the pipeline layout from different positions and angles;quickly query the number,size and distribution of pipelines,instruments and equipment;find out problems that may occur during the processing of reconstruction and expansion easily,and effectively improve the quality and efficiency of the project.The key to the reconstruction of the 3D model of the processing factory is the processing of point cloud data,especially the segmentation and recognition of pipeline point cloud.However,most of the existing point cloud segmentation and pipeline recognition technologies are proposed for the general point cloud model.In fact,it should be noted that very little research on point cloud segmentation and recognition of processing factory has been carried out.The existing methods of point cloud recognition are generally sensitive to noisy data.As a result,the detection accuracy is not high,and it is difficult to evaluate the accuracy of the test results.In view of the problems above,this paper focuses on the key technologies of point cloud segmentation and pipeline identification in processing factory.The main work and innovative achievements are as follows:(1)Review the research status of point cloud segmentation and pipeline identification technology,analyze and evaluate several typical segmentation algorithms,and introduce the application characteristics,application environment and existing problems of each algorithm.(2)Propose an algorithm of the processing factory point cloud segmentation based on Normalized Random Walk.Firstly,the point cloud is simplified using the method of voxel down-sampling.Secondly,the area weighted graph with the node attributes of curvature and node coordinates is created according to the simplified point cloud.Finally,seed points are selected as the starting point interactively,the starting point begins a random walk on the weighted graph until the final steady state distribution is reached,and all non-seed nodes are marked according to the principle of maximum probability,thereby realizing the semantic segmentation of objects of the processing factory point cloud models The related experiments show that the addition of user prior knowledge can reduce the semantic misjudgment and make the segmentation result have better semantic consistencies and robustness,nor does it limit the size and density of point cloud data;The feature of curvature and coordinates as node feature information further reduces the requirements of the method for point cloud data format,which is suitable for segmentation of various point cloud models.(3)Propose a pipeline point cloud detection and recognition algorithm based on feedback Hough transform.Firstly,the point cloud normal vector is computed according to the octreebased k-nearest neighbor search,and the 3D Hough transform is combined with the Gauss map to estimate the initial axial direction of the pipeline.Then,the corrected object according to the initial axis is obtained,and the axial optimization objective function is established.The final axis is obtained by iterative optimization;finally,the axis position and radius are fitted by Hough transform.The related experiments show that The pipeline identification algorithm based on feedback Hough transform effectively improves the estimation accuracy of the parameters such as the axial direction of the pipeline,the optimization objective function proposed in this paper also provides a new evaluation method for the detection results.The related algorithms in this paper can improve the efficiency and design quality of the processing factory 3D model,and greatly reduce the cost of factory renovation or reconstruction.