Research On Substation Scene Segmentation And 3d Point Cloud Object Recognition Technology

With the advancement of 3D laser scanning technology,high-quality point cloud data is now more accessible,providing researchers with comprehensive three-dimensional information about substations.However,substation equipment is made up of numerous pieces with vastly different shapes and characteristics,and their interconnected nature makes it challenging to segment the entire scene using traditional point cloud segmentation methods.Moreover,the lack of publicly available point cloud data for training neural network models further compounds the issue.Additionally,during the point cloud acquisition process,substation scenes often contain occluded objects,resulting in missing and uneven density of the point cloud.All of these factors make it difficult to identify equipment in the scene.To address these challenges,this paper conducts targeted research as follows:(1)This paper proposes a segmentation method for complex substation scenarios.The method extracts the ground point cloud and the power line point cloud based on the spatial characteristics of the ground and power lines in the substation scene,and performs scene segmentation to obtain a separate equipment point cloud.To extract the ground point cloud,the method uses the RANSAC method to simulate the ground plane.The 3D point cloud scene is projected onto a 2D plane to obtain a 2D image of the substation,from which the straight line segments of the power lines are extracted using edge detection and an improved line detection algorithm.The method then filters out the straight line segments those do not belong to the power line by using the surrounding features of the 3D points corresponding to the 2D points on the line.The straight line segment of the power line is extended through the region growing algorithm,and finally,the power line clustering is completed based on the three-dimensional coordinates of the two ends of the power line using Euclidean clustering.This completes the segmentation of the scene.(2)This paper proposes a 3D point cloud object recognition method that combines SHOT features and ESF features.This method identifies models using an approach based on template matching.The first recognition is performed by computing SHOT features of scene and model point cloud.Through the corresponding grouping and Hough voting method,the key points for matching the scene and the model point cloud and the recognition model instance are determined,and the point cloud to be recognized is obtained based on the rotation invariant characteristics of the point cloud,so as to calculate the global feature and perform the second recognition.(3)Finally,the point cloud of the substation scene is obtained through the ground 3D laser scanner,and a model library for identification is constructed.This paper uses the substation cloud and the Kinect model library to test the usability and effectiveness of the segmentation and recognition methods proposed in this paper.Through specific experiments,it is proved that the method proposed in this paper can effectively complete the substation scene segmentation and point cloud object recognition.