|
Abstract Mobile laser scanning can acquire lots of dense point clouds. Therefore, how to get high-quality road point clouds is a problem worthy of further study. This paper proposes a method for automatic extraction of roads from mobile laser scanning point clouds by image semantic segmentation. The authors use a four-step strategy: First, semantic segmentation images are created using 2D panoramic images. Then, fusion and matching are conducted to get rough classification results. After that, the 3D Hough transform is used to get the segmentation plane before fitting. Finally, a finely classified point cloud is obtained through local optimization operations. The authors extracted and evaluated two different points of cloud data on urban roads. The accuracy and integrity are all over 99%. The extraction quality is high enough to adapt the application requirements in practice. The method proposed by the authors can extract road point clouds in different situations and has less primitive constraints on point cloud data. It shows a significant improvement in both universality and robustness compared with other methods.
|
Keywords
road extraction
semantic segmentation
deep learning
fusion and matching
Hough transform
|
|
Corresponding Authors:
Chungeng LI
E-mail: lichungeng@dlmu.edu.cn
|
Issue Date: 14 March 2020
|
|
|
[1] |
Yang B, Wei Z, Li Q . Automated extraction of street-scene objects from mobile LiDAR point clouds[J]. International Journal of Remote Sensing, 2012,33(18):5839-5861.
|
[2] |
Yu Y, Li J, Guan H . Semiautomated extraction of street light poles from mobile LiDAR point-clouds[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015,53(3):1374-1386.
|
[3] |
Wang H, Cai Z, Luo H . Automatic road extraction from mobile laser scanning data[C]// International Conference on Computer Vision in Remote Sensing.IEEE, 2012: 136-139.
|
[4] |
Smadja L, Ninot J, Gavrilovic T . Road extraction and environment interpretation from LiDAR sensors[J]. IAPRS, 2010,38:281-286
|
[5] |
Cabo C, Ordoñez C, García-Cortés S . An algorithm for automatic detection of pole-like street furniture objects from mobile laser scanner point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,87:47-56.
|
[6] |
Yang B, Fang L, Li Q . Automated extraction of road markings from mobile LiDAR point clouds[J]. Photogrammetric Engineering and Remote Sensing, 2012,78(4):331-338.
|
[7] |
Yu Y, Li J, Guan H . Learning hierarchical features for automated extraction of road markings from 3-D mobile LiDAR point clouds[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015,8(2):709-726.
|
[8] |
Jaakkola A, Hyyppä J, Hyyppä H . Retrieval algorithms for road surface modelling using laser-based mobile mapping[J]. Sensors, 2008,8(9):5238-5249.
|
[9] |
Yuan X, Zhao C, Cai Y . Road-surface abstraction using ladar sensing[C]// 10th International Conference on Control,Automation,Robotics and Vision.IEEE, 2008: 1097-1102.
|
[10] |
Zhang W . LiDAR-based road and road-edge detection[C]// Intelligent Vehicles Symposium (IV).IEEE, 2010: 845-848.
|
[11] |
Boyko A, Funkhouser T . Extracting roads from dense point clouds in large scale urban environment[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011,66(6):S2-S12.
|
[12] |
Wu B, Yu B, Huang C . Automated extraction of ground surface along urban roads from mobile laser scanning point clouds[J]. Remote Sensing Letters, 2016,7(2):170-179.
|
[13] |
方莉娜, 杨必胜 . 车载激光扫描数据的结构化道路自动提取方法[J]. 测绘学报, 2013,42(2):260-267.
|
[13] |
Fang L N, Yang B S . Automated extracting structural roads from mobile laser scanning point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2013,42(2):260-267.
|
[14] |
张达, 李霖, 李游 . 基于车载激光扫描的城市道路提取方法[J]. 测绘通报, 2016,(7):30-34,47.
|
[14] |
Zhang D, Li L, Li Y . Urban roads automated extracting from mobile laser scanning point clouds[J]. Bulletin of Surveying and Mapping, 2016,(7):30-34,47.
|
[15] |
刘如飞, 田茂义, 许君一 . 车载激光扫描数据中高速公路路面点滤波[J]. 武汉大学学报(信息科学版), 2015,40(6):751-755.
|
[15] |
Liu R F, Tian M Y, Xu J Y . Expressway road surface point filtering for mobile laser scanning data[J]. Geomatics and Information Science of Wuhan University, 2015,40(6):751-755.
|
[16] |
刘如飞, 卢秀山, 岳国伟 , 等. 一种车载激光点云数据中道路自动提取方法[J]. 武汉大学学报(信息科学版), 2017,42(2):250-256.
|
[16] |
Liu R F, Lu X S, Yue G W , et al. An automatic extraction method of road from vehicle-borne laser scanning point clouds[J]. Geomatics and Information Science of Wuhan University, 2017,42(2):250-256.
|
[17] |
张康, 黑保琴, 李盛阳 , 等. 基于CNN模型的遥感图像复杂场景分类[J]. 国土资源遥感, 2018,30(4):49-55.doi: 10.6046/gtzyyg.2018.04.08.
|
[17] |
Zhang K, Hei B Q, Li S Y , et al. Complex scene classification of remote sensing images based on CNN[J]. Remote Sensing for Land and Resources, 2018,30(4):49-55.doi: 10.6046/gtzyyg.2018.04.08.
|
[18] |
周询, 王跃宾, 刘素红 , 等. 一种遥感影像自动识别耕地类型的机器学习算法[J]. 国土资源遥感, 2018,30(4):68-73.doi: 10.6046/gtzyyg.2018.04.11.
|
[18] |
Zhou X, Wang Y B, Liu S H , et al. A machine learning algorithm for automatic identification of cultivated land in remote sensing images[J]. Remote Sensing for Land and Resources, 2018,30(4):68-73.doi: 10.6046/gtzyyg.2018.04.11.
|
[19] |
Chen L C, Zhu Y, Papandreou G , et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[EB/OL]. (2018- 08- 22)[2018-12-28]. http://arxiv.org/abs/1802.02611v1.
url: http://arxiv.org/abs/1802.02611v1
|
[20] |
李金洪 . 深度学习之TensorFlow入门、原理及进阶实战[M]. 北京: 机械工业出版社, 2018.
|
[20] |
Li J H. Getting Started and Best Practices with TensorFlow for Deep Learning[M]. Beijing: China Machine Press, 2018.
|
[21] |
Wang H, Wang C, Chen Y . Extracting road surface from mobile laser scanning point clouds in large scale urban environment[C]// 17th International Conference on Intelligent Transportation Systems(ITSC).IEEE, 2014: 2912-2917.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|