| 车载激光扫描数据中实线型交通标线提取 |
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| 引用本文： | 方莉娜,黄志文,罗海峰,陈崇成.车载激光扫描数据中实线型交通标线提取[J].测绘学报,2019,48(8):960-974. |
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| 作者姓名： | 方莉娜 黄志文 罗海峰 陈崇成 |
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| 作者单位： | 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;空间数据挖掘与信息共享教育部重点实验室,福建 福州 350002;福州大学数字中国研究院,福建 福州 350002;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;空间数据挖掘与信息共享教育部重点实验室,福建 福州 350002;福州大学数字中国研究院,福建 福州 350002;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;空间数据挖掘与信息共享教育部重点实验室,福建 福州 350002;福州大学数字中国研究院,福建 福州 350002;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;空间数据挖掘与信息共享教育部重点实验室,福建 福州 350002;福州大学数字中国研究院,福建 福州 350002 |
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| 基金项目： | 国家自然科学基金青年基金（41501493）；福建省自然科学基金（2017J01465）；中国博士后科学基金（2017M610391）；福建省教育厅中青年教师科研项目（JAT160078） |
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| 摘 要： | 本文提出一种基于路面点云强度增强的车载激光点云实线型交通标线提取方法。首先通过预处理提取路面点云,获取各激光点与轨迹线的距离。然后逐段对路面进行强度增强,集合多滤波器集成的策略进行强度变换和去噪,消除距离、点密度、磨损等因素对反射强度值影响,增强路面点云和标线的强度差异。基于增强后的反射强度,采用k均值聚类和连通分支聚类等方法对标线进行分割,并利用归一化图割方法优化强度分割结果。最后利用实线型标线的语义信息和空间分布特征从分割后标线对象中识别实线型交通标线。试验采用四份不同车载激光扫描系统获取的数据用于验证本文方法有效性,实线型标线提取结果的准确率达到95.98%,召回率达到91.87%,综合评价指标F 1-Measure值达到95.55%以上。试验结果表明本文方法能够有效增强受扫描距离、路面磨损及点密度分布不均等因素影响的点云强度信息,实现不同车载激光扫描获取的复杂道路环境下实线型交通标线的提取。
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| 关 键 词： | 车载激光点云 强度增强 标线提取 强度分割 实线型标线提取 |
| 收稿时间： | 2018-12-12 |
| 修稿时间： | 2019-04-24 |
Solid lanes extraction from mobile laser scanning point clouds |
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| FANG Lina,HUANG Zhiwen,LUO Haifeng,CHEN Chongcheng.Solid lanes extraction from mobile laser scanning point clouds[J].Acta Geodaetica et Cartographica Sinica,2019,48(8):960-974. |
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| Authors: | FANG Lina HUANG Zhiwen LUO Haifeng CHEN Chongcheng |
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| Institution: | 1. National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou 350002, China;2. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350002, China;3. Academy of Digital China, Fuzhou University, Fuzhou 350002, China |
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| Abstract: | This paper presented a novel method for solid lanes extraction from Mobile laser scanning (MLS) point clouds. The proposed method firstly removed the off-ground point clouds and then calculated the scanning distance between the points of road surface and sensors. Then, the reflective intensity data of road surface were transformed into relative values to overcome the influence of the scanning distance, the points' density, abrasion and roughness of road surface block by block. After the intensity enhancement, road markings were separated from the road surface based on the k-means clustering and connected component. In order to deal with the problem of under-segmentation and over-segmentation caused by the adhesion of solid lines and stop lines or other entrance markings, some features of geometric shape and the spatial distribution were then used to refine the results of intensity segmentation by the Normalized Cuts. Finally, the semantic structure information of road markings was explored to separate the solid lines from other road markings like zebra crossings, dashed lines. Experiments were undertaken to evaluate the validities of the proposed method with four test data sets acquired from different MLS systems. Quantitative evaluations on four MLS data sets indicated that the proposed method achieved a Precision, Recall and F1-Measure of 95.98%, 91.87% and 95.55%, respectively, which validated that the proposed method has achieved promising performance. |
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| Keywords: | MLS points cloud the intensity enhancement road markings extraction intensity segmentation solid lanes detection |
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