顾及分层动态区域增长的车载LiDAR点云行道树提取方法

A Hierarchical Dynamic Region Growing Method for Extracting Street Trees from Vehicle LiDAR Point Cloud Data

行道树的准确提取对生态园林城市建设及城市智慧化发展具有重要意义。但车载LiDAR点云数据中经常出现行道树与近邻地物相互遮掩、连接的情况,从而导致无法准确进行行道树提取。针对这一问题,本文提出一种分层动态区域增长行道树提取方法。首先,通过点云栅格化滤除地面点并根据地物投影特征进行行道树初步提取。然后,根据地物分布特征对点云数据进行等高度分层处理,构建层次化点云空间,进一步获取行道树与干扰地物信息。接着,在层次化点云空间内部进行动态区域增长,获取同一层和相邻层之间的点云属性信息,生成点云聚类簇以区分行道树与干扰地物。最后,根据干扰地物的几何特征和行道树杆状特征,滤除干扰地物实现准确的行道树提取。本文选用激光雷达大会提供的竞赛数据及Open DataLab官网提供的里尔、巴黎两地区街道点云数据进行实验。实验结果表明,本文方法行道树提取的正确率与完整率分别在98.69与97.73之上。本文方法能够在行道树与近邻地物相互遮掩、连接的情况下实现准确完整的行道树提取。同时,本文分层动态区域增长行道树提取方法的数据适用性更强,并且可以在行道树独立性不强的情况下有效提取行道树。

The accurate extraction of street trees is of great significance for the construction of ecological garden cities and the promotion of urban intelligence.However,in vehicle LiDAR point cloud data,there are many situations where the street tree and the adjacent ground objects,such as buildings or other vegetation,are occluded or connected to each other.These situations make it difficult to accurately extract street trees.To solve the above problems,a hierarchical dynamic region-growing method for street tree extraction was proposed in this paper.Firstly,the ground points were filtered out using the point cloud rasterization method,and street trees were preliminarily extracted according to the projection features of ground objects.Secondly,the point cloud data was stratified with equal height intervals based on the distribution characteristics of ground objects.A hierarchical point cloud space was constructed to further obtain information about street trees and interfering objects.Then,the dynamic region-growing operation was carried out in the hierarchical point cloud space,and the attribute information of the point cloud between the same layer and the adjacent layer was obtained.Based on the obtained attribute information,point cloud clusters were generated to distinguish street trees from interfering objects.Finally,the interfering objects were filtered out to achieve accurate street tree extraction using the geometric features of the interfering objects and the rod-like features of the street trees.In this paper,competition data provided by LiDAR conference and point cloud data provided by Open DataLab website were selected for our experiment.The street data of Lille and Paris from the Open DataLab website were mainly used.The experimental results demonstrate that the accuracy and completeness of the proposed method for extracting street trees are above 98.69% and 97.73%,respectively.The proposed method can achieve accurate and complete street tree extraction even when street trees and adjacent ground objects are occluded or connected with each other.Overall,the experimental results strongly support the effectiveness and reliability of the proposed method for street tree extraction.Furthermore,the hierarchical dynamic region-growing method proposed in this paper for street tree extraction demonstrates remarkable data applicability.This method exhibits a robust capacity to adapt to different datasets,making it suitable for a wide range of scenarios.Notably,even when the independence of street trees is not immediately evident,the proposed approach can still effectively extract them.