光电工程, 2018, 45 (12): 180266, 网络出版: 2018-12-18
一种城市环境三维点云配准的预处理方法
A preprocessing method of 3D point clouds registration in urban environments
点云预处理 地面分割 点云去噪 帧间匹配 point cloud preprocessing ground segmentation point cloud density clustering inter-frame matching
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。
Abstract
Aiming at the problem that 3D 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.
赵凯, 徐友春, 王任栋. 一种城市环境三维点云配准的预处理方法[J]. 光电工程, 2018, 45(12): 180266. Zhao Kai, Xu Youchun, Wang Rendong. A preprocessing method of 3D point clouds registration in urban environments[J]. Opto-Electronic Engineering, 2018, 45(12): 180266.