[1]蒋腾平,王永君,陶帅兵,等.功能性规则约束下的三维点云道路设施语义标注[J].南京师范大学学报(工程技术版),2018,18(04):052.[doi:10.3969/j.issn.1672-1292.2018.04.008]
　Jiang Tengping,Wang Yongjun,Tao Shuaibing,et al.3D Point Cloud Road Facilities Semantic LabelingConstrained by Functional Rules[J].Journal of Nanjing Normal University(Engineering and Technology),2018,18(04):052.[doi:10.3969/j.issn.1672-1292.2018.04.008]
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功能性规则约束下的三维点云道路设施语义标注
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
18卷
期数:
2018年04期
页码:
052
栏目:
计算机工程
出版日期:
2018-12-30

文章信息/Info

Title:
3D Point Cloud Road Facilities Semantic LabelingConstrained by Functional Rules
文章编号:
1672-1292(2018)04-0052-07
作者:
蒋腾平1; 2; 王永君1; 2; 陶帅兵1; 2; 李云莉1; 2; 刘 姗3
(1.南京师范大学虚拟地理环境教育部重点实验室,江苏 南京 210023)(2.江苏省地理信息资源开发与利用协同创新中心,江苏 南京 210023)(3.厦门大学海洋与地球学院,福建 厦门 361102)
Author(s):
Jiang Tengping1; 2; Wang Yongjun1; 2; Tao Shuaibing1; 2; Li Yunli1; 2; Liu Shan3
(1.Key Laboratory of Virtual Geographic Environment of Ministry of Education,Nanjing Normal University,Nanjing 210023,China)(2.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China)(3.College of Ocean and Earth Science,Xiamen University,Xiamen 361102,China)
关键词:
道路设施解译; 路灯; 交通标志; 移动激光扫描; 点云
Keywords:
road facility interpretation; street lights; traffic sign; mobile laser scanning; point cloud
分类号:
P237
DOI:
10.3969/j.issn.1672-1292.2018.04.008
文献标志码:
A
摘要:
三维场景的语义标注研究是机器视觉、摄影测量以及机器学习等领域的热门研究课题. 但基于移动激光扫描数据的道路设施精确解释仍处于瓶颈期. 提出一种基于逻辑关系和功能性对道路设施进行语义标注的新方法,先总结制定道路设施相关的特征符号和规则,再根据所定义的规则功能对点云数据进行语义标注. 基于该方法对国内某中等城市道路点云数据进行了相当详尽的解释,正确提取了93%的杆状物体,并全部正确识别. 对于杆状物体的附件(如灯头、交通标志等),基本正确识别且有效标记. 与改进的RANSAC算法相比,该方法提供了一个较好的解决方案,有助于在城市环境中自动绘制详细的道路设施.
Abstract:
Nowadays 3D scene labeling has become a hot topic in the reform of machine learning,photogrammerty,computer vision,etc. Road facility semantic labelling is vital for large scale mapping and autonomous driving systems. This paper proposes a new method for semantic tagging of road facilities based on logical relationships and functionality. We first summarize the relevant feature symbols and rules for road facilities,and then we semantically tag the point cloud data according to the defined rules. Based on the method proposed by us,a detailed explanation of road point cloud data in a medium-sized city in China is conducted. 93% of rod-shaped objects are correctly extracted and correctly identified. For attachments to rod-shaped objects(such as lamp heads,traffic signs,etc.),they are basically correctly identified and effectively marked. Compared with the improved RANSAC algorithm,our framework provides a more promising solution to automatically map road furniture at a detailed level in urban environments.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-04-26.
基金项目:国家自然科学基金(41771439)、国家重点研发计划项目(2016YFB0502300)、江苏省研究生科研与实践创新计划项目(KYCX18_1206).
通讯联系人:王永君,博士,副教授,研究方向:三维GIS. E-mail:wangyongjun@njnu.edu.cn
更新日期/Last Update: 2018-12-30