一种城市环境三维点云配准的预处理方法
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摘要
针对城市三维环境下LiDAR点云数据密度大、离群噪点多、分布散乱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三维LiDAR点云帧间匹配的预处理方法。首先,将点云数据转化为均值高程图,利用网格之间的高度梯度对点云进行地面分割处理;然后,通过三维体素栅格划分的方法改进了DBSCAN聚类算法,用改进后的VG-DBSCAN对点云进行聚类,聚类后目标点云与离群点分离,从而剔除点云中的离群噪点;最后,采用Voxel Grid滤波器对点云降采样。实验结果表明,所提方法可以对点云数据进行实时的预处理,平均耗时为132.1 ms;预处理之后点云帧间匹配的精确度提高了2倍,平均耗时也仅为预处理前的1/6。
Aiming at the problem that 3 D LiDAR point cloud has high data density, outlier noise, and scattered distribution in urban environment, which is not conducive to the matching between point clouds in the later stage, a pre-processing method for large-scale LiDAR point cloud frame matching in urban environments is proposed. First, the point cloud data is transformed into a Mean Elevation Map, and the ground point segmentation processing is performed on the point cloud using the height gradient between the grids; then, the DBSCAN clustering algorithm is improved by the three-dimensional voxel grid division method, and the improved VG-DBSCAN is used to cluster point clouds and separate the target point cloud from the outliers after clustering, thereby, which eliminates outlier noises in the point cloud. Finally, the Voxel Grid filter is used to down sample the point cloud. The experimental results show that the proposed method can perform real-time preprocessing on point cloud data, and the average time is 132.1 ms. After pre-processing, the accuracy of point cloud frame matching is increased by 2 times, and the average time consumption is only 1/6 before pre-processing.
Aiming at the problem that 3 D LiDAR point cloud has high data density, outlier noise, and scattered distribution in urban environment, which is not conducive to the matching between point clouds in the later stage, a pre-processing method for large-scale LiDAR point cloud frame matching in urban environments is proposed. First, the point cloud data is transformed into a Mean Elevation Map, and the ground point segmentation processing is performed on the point cloud using the height gradient between the grids; then, the DBSCAN clustering algorithm is improved by the three-dimensional voxel grid division method, and the improved VG-DBSCAN is used to cluster point clouds and separate the target point cloud from the outliers after clustering, thereby, which eliminates outlier noises in the point cloud. Finally, the Voxel Grid filter is used to down sample the point cloud. The experimental results show that the proposed method can perform real-time preprocessing on point cloud data, and the average time is 132.1 ms. After pre-processing, the accuracy of point cloud frame matching is increased by 2 times, and the average time consumption is only 1/6 before pre-processing.
引文
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[3]Biosca J M,Lerma J L.Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods[J].ISPRS Journal of Photogrammetry and Remote Sensing,2008,63(1):84-98.
[4]Himmelsbach M,Hundelshausen F V,Wuensche H J.Fast segmentation of 3D point clouds for ground vehicles[C]//Proceedings of 2010 IEEE Intelligent Vehicles Symposium,2010:560-565.
[5]Moosmann F,Pink O,Stiller C.Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion[C]//Proceedings of 2009 IEEE Intelligent Vehicles Symposium,2009:215-220.
[6]Zhang M F,Fu R,Guo Y S,et al.Road segmentation method based on irregular three dimensional point cloud[J].Journal of Jilin University(Engineering and Technology Edition),2017,47(5):1387-1394.张名芳,付锐,郭应时,等.基于三维不规则点云的地面分割算法[J].吉林大学学报(工学版),2017,47(5):1387-1394.
[7]Fleishman S,Drori I,Cohen-Or D.Bilateral mesh denoising[J].ACM Transactions on Graphics,2003,22(3):950-953.
[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):2329-2334,2341.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):2329-2334,2341.
[10]Siciliano B,Khatib O.Springer Handbook of Robotics[M].Berlin,Heidelberg:Springer-Verlag,2007.
[11]Ester M,Kriegel H P,Sander J,et al.A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining,1996:226-231.
[12]Rusu R B,Cousins S.3D is here:Point Cloud Library(PCL)[C]//Proceedings of IEEE International Conference on Robotics and Automation,2011:1-4.