采用车载激光雷达的带状地物矢量化与结构特征提取  

Vectorization and Structural Feature Extraction of Strip Objects Using Vehicle-Mounted LiDAR

作　　者：方莉娜[1,2,3] 卢丽靖 赵志远 陈崇成[1,2,3] FANG Lina;LU Lijing;ZHAO Zhiyuan;CHEN Chongcheng(National Engineering Research Centre of Geospatial Information Technology,Fuzhou University,Fuzhou 350002,China;Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education,Fuzhou University,Fuzhou 350002,China;Academy of Digital China,Fuzhou University,Fuzhou 350002,China)

机构地区：[1]福州大学地理空间信息技术国家地方联合工程研究中心,福建福州350002 [2]福州大学空间数据挖掘与信息共享教育部重点实验室,福建福州350002 [3]福州大学数字中国研究院,福建福州350002

出　　处：《华侨大学学报（自然科学版）》2020年第6期797-807,共11页Journal of Huaqiao University(Natural Science)

基　　金：国家自然科学基金青年基金资助项目(41501493);福建省自然科学基金资助项目(2017J01465);中国博士后科学基金资助项目(2017M610391)。

摘　　要：针对车载激光点云带状地物形状多样、难以用规则语义信息矢量化,以及结构语义信息提取研究较少的问题,提出一种基于Ribbon Snake模型的车载激光雷达带状地物(道路边界、实线型标线、铁轨)矢量化与结构特征提取方法.首先,通过格网剖分构建点云特征图,利用Ribbon Snake模型提取带状地物矢量化数据;然后,分析不同道路和铁路场景的结构特征,生成具有准确几何和拓扑结构信息的三维矢量数据和属性数据.实验表明:该方法能够准确地提取带状地物矢量化与结构信息,实现不同场景下带状地物的有效完整描述.Due to the diverse shapes of strip objects,it is difficult to use regular semantic information for vectorization,and there is less research on structural semantic information extraction,a novel method for vectorization and structural feature extraction of strip objects(road boundary,solid line marking,railroad track)from vehicle-mounted LiDAR based on Ribbon Snake model is proposed.Firstly,a point cloud feature map was generated based on the salient strip points,and the Ribbon Snake model is used to extract the vectorization boundaries of the strip objects.Then analyze the structural characteristics of different road and railway scenes to generate 3D vector data and attribute data with accurate geometric and topological structure information.Experiments showed that the proposed method has accurately gained vectorization and structure information of strip objects,and achieved effective and complete description of strip objects in various scenes.

关 键 词：车载激光雷达 Ribbon Snake模型 带状地物 矢量化 结构特征提取 

分 类 号：P208[天文地球—地图制图学与地理信息工程]