التفاصيل البيبلوغرافية
| العنوان: |
基于车载激光点云的道路交叉口检测与识别. (Chinese) |
| Alternate Title: |
Road intersection detection and recognition based on mobile laser scanning. (English) |
| المؤلفون: |
方莉娜, 王康 |
| المصدر: |
Journal of Nanjing University of Information Science & Technology (Natural Science Edition) / Nanjing Xinxi Gongcheng Daxue Xuebao (ziran kexue ban); 2021, Vol. 13 Issue 6, p635-644, 10p |
| Abstract (English): |
Road intersections are important parts of road traffic network, the location and type of which are the basic data for various application services such as high-definition map and automatic driving. However, little attention has been paid to classify road intersections compared with the great number of researches on the road boundary ex traction from mobile laser scanning point clouds. Here, we propose a road intersection classification method based on dynamic graph neural network. First, we employ geometric and spatial distribution differences of supervoxels to extract road boundaries from ground. Then we calculate the curvature of road boundary points and detect road inter sections according to the curvature difference in sliding windows. Finally, we build a dynamic graph neural network to identify the T junction and regular intersections. The experimental results show the proposed method can accurately detect most road intersections. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
道路交叉口是道路交通网的重要组成部分,其位置和类型是高精地图、自动驾驶等应用服务的基础数据.目前研究多关注车载激光点云的道路边界提取,较少关注道路交叉口类型识别.为此,本文提出一种基于动态图神经网络的道路交叉口分类方法.首先分析地面超体素的几何和空间分布差异进行提取道路边界点;然后计算道路边界点曲率,利用滑动窗口中曲率差异检测道路交叉口;最后构建动态图神经网络识别出"T"型和"十"型道路交叉口.实验采用两份不同车载激光点云数据验证本文方法的有效性,实验结果表明,该方法能准确检测绝大多数道路交叉口位置及类型. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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