Point Cloud Registration Network Based on Edge Convolution
The structure of underground roadway is narrow and there are many branches,and the point cloud obtained in underground tunnels needs to be registered to obtain complete data.Traditional point cloud registration methods require high in-itial position of the point cloud and have multiple calculation iterations,but the registration effect is poor and the calculation is slow in the complex environment and huge amount of data in the scenic spot cloud of underground roadway.Therefore,based on deep learning technology and PCRNet,combined with the advantages of edge convolutional networks in local feature extraction,a point cloud direct registration network DGRNet based on edge convolution is constructed.The network uses edge convolution to check the input point cloud for feature extraction in the feature extraction module,which can better learn the complex feature changes and geometric structure of 3D point clouds,improved understanding of local features in the scene.The experimental re-sults show that the DGRNet network has better overall registration accuracy in object models compared to other networks,and can maintain stable registration accuracy under the influence of point cloud noise,with good robustness.The four error results of DGRNet in the roadway point cloud registration scene are all the smallest,and compared with PCRNet,the error results have decreased by 19.0％,20.1％,24.2％,and 21.0％,respectively.It is indicated that the DGRNet network can perform high-pre-cision point cloud registration,providing a new method for point cloud registration in complex underground scenes.
point cloud registrationdeep learning3D laser scanning roadwayPCRNetDGRNet
万方数据
维普
