基于椭圆柱面模型的隧道点云滤波方法

An Elliptic Cylindrical Model for Tunnel Filtering

由于地铁盾构环片附着了大量的螺栓和螺丝以及隧道内壁上安装的大量金属支架、电器设备等附属物,使得获取的激光点云数据包含了大量的非隧道内壁点(以下简称非点),从而影响到隧道点云在形变监测、三维建模等方面的应用。本文提出基于区域分割的椭圆柱面模型方法来滤除非点,将地铁隧道横截面视为椭圆(根据盾构施工特点),利用获取的隧道原始点云数据提取出隧道中轴线,并沿隧道中轴线正交方向将点云分割为等间隔区域,然后利用各区域的点云分别迭代拟合为椭圆柱面,从而实现对隧道内壁非点的自动滤除。实验结果表明,该方法能够有效滤除隧道内的非点,为三维激光扫描技术用于地铁隧道形变监测提供高质量的点云数据。

The large number of bolts and screws attached to the subway shield ring plates,along with the great amount of accessories of metal stents and electrical equipments mounted on the tunnel walls,means that tunnel laser point cloud data includes lots of non-tunnel section points,referred to as nonpoints,therefore affecting the accuracy for modeling and deformation monitoring.This paper proposes a filtering method for point clouds based on the elliptic cylindrical model.The original laser point cloud data is projected onto a horizontal plane,and a searching algorithm is used to extract the edging points of both sides,to further to fit the tunnel central axis.Along the axis the point cloud is segmented regionally,and then fitted as smooth elliptic cylindrical surface by iteration.This processing enables automatic filtering of those inner wall non-points.Experiments on two groups of data showed coincident results,that the elliptic cylindrical model based method effectively filters out the non-points,thus providing high-quality point cloud for subway deformation monitoring.