Research On The Rigid Registration Algorithms Of Ground-based LiDAR Point Cloud

With a rapid development of Li DARs(Light Detection and Ranging),3D point cloud has been widely applied in many fields,such as automatic driving,robot,remote sensing,medical treatment,industrial design,and cultural relics protection.However,during data acquisition,many factors,such as projection,visual blind spot and complex surface,determine that the data acquisition of the whole object/scene is impossibly completed by only one single measurement.In order to obtain the complete 3D point cloud of the object/scene,it is necessary to align the3 D point clouds collected from different perspectives to the same coordinate system through rotation and translation,and splice them into a complete 3D point cloud.Such above process is called point cloud registration.As a basic task of 3D point cloud data processing,point cloud registration plays an important role in 3D reconstruction,object recognition and tracking,mapping and localization,object capture,and other applications.The focus of this paper is on the rigid registration of ground-based Li DAR point cloud,which mainly depends on the information of the overlapping area between two partially overlapped point clouds to find correspondences or calculate the rigid transformation parameters.However,in fact,rigid registration of ground-based Li DAR point cloud often faces various difficulties,such as noise,non-overlapping areas,density variation,symmetrical objects or repeating structures,which brings great challenges to existing registration algorithms.To effectively solve above problems,this paper carried out a series of research on the point cloud registration in the aspects of traditional methods and deep learning methods.Details are as follows:Firstly,to deal with the information loss of 2D projection descriptors and the sensitivity to density variation and boundaries of 3D voxel descriptors,a voxel-based buffer-weighted binary descriptor(VBBD)based registration method is proposed.This descriptor has the following advantages:(1)3D information can be directly obtained,which can avoid the information compression caused by 2D projection,so that it has better descriptiveness;(2)the voxel is binarized by the weighted density of the buffer,which improves the robustness to boundary,noise and density variation.On the basis of this descriptor,a KM(Kuhn-Munkres)algorithm and a RANSAC(Random Sample Consensus)algorithm are combined for feature matching and parameters solving,respectively.A set of experimental results on object dataset,indoor dataset and outdoor dataset show that this VBBD based registration method can effectively improve the computational efficiency and is of high robustness;Secondly,considering that effective information of registration is mainly concentrated in the overlapping area and the points in non-overlapping areas greatly decrease the registration accuracy,an attention-based overlapping area detection network,called Mask Net++,is proposed.By simultaneously calculating two binary mask vectors of two partially overlapped point clouds,the network can obtain the inliers(overlapping areas)describing the same geometry of the same object/scene at the same time.A set of experiments on synthetic object dataset and real-world outdoor dataset demonstrate that the Mask Net++ can detect overlapping areas in high accuracy.Mask Net++ can be widely applied in(1)point cloud denoising,which can effectively filter a large number of random noise in high accuracy;(2)point cloud registration,which can greatly improve the performance of partial-to-partial registration by acting as a preprocess to estimate overlapping areas when deal with partial point clouds;Finally,to deal with the problem that the existing deep learning based registration methods perform poorly in partial-to-partial point cloud registration,inspired by Mask Net++,a pose regression network based on overlapping area information interaction,named SCANet,is proposed.The spatial self-attention aggregation module(SSA)is introduced into the feature extraction subnetwork to effectively extract the global information of different levels,and the channel cross-attention regression module(CCR)is used in the pose estimation subnetwork for the information exchange between the two global features while pose regression.The network has few parameters,directly outputs the transformation parameters.It is fully differentiable,directly processes point clouds,and does not involve the solution of point correspondence,which can effectively avoid the matching ambiguity of symmetrical objects and repeated structures.A set of experiments on synthetic object dataset and real-world outdoor dataset show that the SCANet with overlapping area information interation achieves the state-of-the-art performance in accuracy and efficiency compared with the existing traditional registration methods and deep learning based registration methods.