基于VG-DBSCAN算法的大场景散乱点云去噪
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摘要
针对城市环境下三维激光雷达(LiDAR)点云数据密度不均匀、离群噪点多而不利于后期点云帧间匹配的问题,提出一种应用于城市环境下大规模散乱LiDAR点云的离群噪点滤除算法。该算法对传统的基于密度的噪声应用空间聚类(DBSCAN)算法进行改进,通过对三维点云进行体素栅格划分,创建了一个由栅格单元组成的集合,以此大幅减小每个对象在数据空间中邻域的搜索范围。改进后的算法能够快速发现各个聚类,使目标点云与离群点分离,从而剔除点云中的离群噪点。实验结果表明:所提算法能够实时处理点云数据,在保证点云三维几何特征的同时能有效识别并滤除点云中的离群噪点,降低点云规模,加快点云后续处理的效率,使帧间匹配的精确度提高了2倍,且匹配耗时仅为去噪处理前的1/3。
Non-uniform 3 Dlight detection and ranging(LiDAR)point-cloud data with outlier noises are not conducive to interframe point-cloud-matching in urban environments.Thus,an outlier noise filtering algorithm for large-scale scattered LiDAR point-cloud in urban environments is proposed.This algorithm improves the traditional density-based spatial clustering of applications with noise(DBSCAN)algorithm by applying voxel-grid partitioning to the three-dimensional point-cloud to create a set of grid cells,which greatly reduces the search scope of each object′s neighborhood in the data-space range.The improved algorithm can quickly find each cluster,which separates the target point-cloud from the outliers,thus eliminating the outlier noise in the point-cloud.The experimental results show that the proposed algorithm can process point-cloud data in real-time,ensure threedimensional geometric features of point-cloud,effectively recognize and filter out outlier noise,reduce the scale of point-cloud,and speed up the subsequent processing efficiency of the point-cloud.Using this algorithm,the accuracy of matching between the frames is doubled,and the matching time is only one-third of the time before denoising.
Non-uniform 3 Dlight detection and ranging(LiDAR)point-cloud data with outlier noises are not conducive to interframe point-cloud-matching in urban environments.Thus,an outlier noise filtering algorithm for large-scale scattered LiDAR point-cloud in urban environments is proposed.This algorithm improves the traditional density-based spatial clustering of applications with noise(DBSCAN)algorithm by applying voxel-grid partitioning to the three-dimensional point-cloud to create a set of grid cells,which greatly reduces the search scope of each object′s neighborhood in the data-space range.The improved algorithm can quickly find each cluster,which separates the target point-cloud from the outliers,thus eliminating the outlier noise in the point-cloud.The experimental results show that the proposed algorithm can process point-cloud data in real-time,ensure threedimensional geometric features of point-cloud,effectively recognize and filter out outlier noise,reduce the scale of point-cloud,and speed up the subsequent processing efficiency of the point-cloud.Using this algorithm,the accuracy of matching between the frames is doubled,and the matching time is only one-third of the time before denoising.
引文
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[3] Chen G B,Gao Z H,He L.Step-by-step automatic calibration algorithm for exterior parameters of 3D lidar mounted on vehicle[J].Chinese Journal of Lasers,2017,44(10):1010004.陈贵宾,高振海,何磊.车载三维激光雷达外参数的分步自动标定算法[J].中国激光,2017,44(10):1010004.
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[5] Xiong F G,Huo W,Han X,et al.Removal method of mismatching keypoints in 3Dpoint cloud[J].Acta Optica Sinica,2018,38(2):0210003.熊风光,霍旺,韩燮,等.三维点云中关键点误匹配剔除方法[J].光学学报,2018,38(2):0210003.
[6] Fleishman S,Drori I,Cohen-Or D.Bilateral mesh denoising[J]. ACM Transactions on Graphics,2003,22(3):950-953.
[7] Nie J H,Hu Y,Ma Z.Outlier detection of scattered point cloud by classification[J].Journal of ComputerAided Design&Computer Graphics,2011,23(9):1526-1532.聂建辉,胡英,马孜.散乱点云离群点的分类识别算法[J].计算机辅助设计与图形学学报,2011,23(9):1526-1532.
[8] Li R Z, Yang M, Ran Y,et al.Point cloud denoising and simplification algorithm based on method library[J]. Laser&Optoelectronics Progress,2018,55(1):011008.李仁忠,杨曼,冉媛,等.基于方法库的点云去噪与精简算法[J].激光与光电子学进展,2018,55(1):011008.
[9] Su B Y,Ma J Y,Peng Y S,et al.Algorithm for RGBD point cloud denoising and simplification based on K-means clustering[J].Journal of System Simulation,2016,28(10):2329-2334.苏本跃,马金宇,彭玉升,等.基于K-means聚类的RGBD点云去噪和精简算法[J].系统仿真学报,2016,28(10):2329-2334.
[10]Ester M,Kriegel H P,Xu X.A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C].International Conference on Knowledge Discovery and Data Mining, Oregon,1996:226-231.
[11]Rusu R B,Cousins S.3Dis here:point cloud library(PCL)[J].Proceedings of the IEEE,2011,47(10):1-4.