全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用：99美元
投递稿件

查看量下载量

相关文章

更多...

利用激光雷达点云生成城市级三维道路地图
Combine Laser Scan Data with Open Street Map to Produce a Three-Dimensional Road Map

DOI: 10.12677/CSA.2019.96132, PP. 1169-1182

Keywords: 三维重建，激光雷达，霍夫变换，三维地图
3D Reconstruction, Lidar, Hough Transform, 3D Map

Full-Text   Cite this paper   Add to My Lib

Abstract:

本文提出了结合激光雷达点云拓展开源地图(Open Street Map, OSM)生成城市三维道路地图的算法，称为PHT (Projection and Hough Transform)。该方法可分为室外通道处理，室内通道处理和坡度异常再计算三部分。室外通道主要是基于局部路面可用平面近似的假设，利用正交投影将三维道路投影成直线，霍夫变换(Hough Transform, HT)提取道路候选点集合，再拟合平面计算高度；室内通道主要是基于相关联的室外通道的高度由投影距离加和得到。最后针对坡度异常的道路，利用带权重的霍夫变换(Weighted Hough Transform, WHT)再计算。本文使用德国科隆市政府提供的机载激光雷达点云(误差约为20 cm)，为亚琛市建立了三维道路地图。结果表明，与OPTICS算法(Ordering Points to Identify The Clustering Structure)相比，PHT成功预测了87%的场景，大于OPTICS算法13%的成功率；该算法准确率更高，对于点云被遮盖的情况，点云密度的变化以及噪声点的干扰更具有鲁棒性。
With the continuous development of computer technology, the method to acquire spatial data has updated rapidly. Three-dimensional digital map attracts so much attention to be developed. Generating a three-dimensional digital map requires a basic map. Because the Open Street Map (OSM) is open-source and free, it has received widespread attention. However, the height information of the road is very sparse in the OSM, and the mean square error is higher than 5 meters, which makes more and more researchers focus on the generation of high-precision three-dimensional maps. Due to the Light Detection and Ranging (LiDAR) point cloud’s high-precision characteristics whose average square error is about 20 cm, it can extend the OSM to generate high-precision 3D maps. This paper studies the method of OSM combined with LiDAR point cloud to generate a three-dimensional digital map. Due to the sampling characteristics of the airborne LiDAR used in the overhead view, the occluded area cannot be sampled. The method proposed in this paper can solve the challenge of occlusion. It is composed of 3 main parts: 1) dealing with indoor area; 2) handling with outdoor area; 3) applied Weighted Hough Transform (WHT) for recalculation. The main steps for dealing with indoor area are as follows: 1) The three-dimensional road surface is projected into a two-dimensional line by orthogonal projection. 2) To find a set of road candidate points, the line is fitted by Hough Transform (HT). 3) Random Sampling the Uniform Sample Consensus (RANSAC) combined with the least squares method (LSM) is used to fit the road plane according to the obtained set of candidate points. This paper proposes a method for estimating the height of an indoor road using the height of the associated outdoor channel which is added up with different weights according to their projection distance. For the road with abnormal slope, the Weighted Hough Transform (WHT) is used for recalculation. This paper uses the airborne lidar point cloud (root mean square

References

[1]  [1] D？afi？, D., Schoonbrood, P., Franke, D., et al. (2017) eNav: A Suitable Navigation System for the Disabled. Ambient Assisted Living. Springer International Publishing, Berlin, 133-150.
https://doi.org/10.1007/978-3-319-52322-4_9
[2]  Stal, C., Lonneville, B., Maeyer, P.D., et al. (2016) Procedural City Model Using Multi-Source Parameter Estimation. International Conference on Geographical Information Systems Theory, Barcelona, 28-30 April 2015, 1-6.
https://doi.org/10.5220/0005466602330238
[3]  Babahajiani, P., Fan, L., K？m？r？inen, J.-K., et al. (2017) Urban 3D Segmentation and Modelling from Street View Images and LiDAR Point Clouds. Machine Vision and Applications, 28, 679-694.
https://doi.org/10.1007/s00138-017-0845-3
[4]  Hu, X., Li, X. and Zhang, Y. (2013) Fast Filtering of LiDAR Point Cloud in Urban Areas Based on Scan Line Segmentation and GPU Acceleration. IEEE Geoscience and Remote Sensing Letters, 10, 308-312.
https://doi.org/10.1109/LGRS.2012.2205130
[5]  Soilán, M., Truong-Hong, L., Riveiro, B., et al. (2018) Auto-matic Extraction of Road Features in Urban Environments Using Dense ALS Data. International Journal of Applied Earth Observation and Geoinformation, 64, 226-236.
https://doi.org/10.1016/j.jag.2017.09.010
[6]  Rodríguez-Cuenca, B., et al. (2015) An Approach to Detect and De-lineate Street Curbs from MLS 3D Point Cloud Data. Automation in Construction, 51, 103-112.
https://doi.org/10.1016/j.autcon.2014.12.009
[7]  Zai, D., Guo, Y., Li, J., et al. (2016) 3D Road Surface Extraction from Mobile Laser Scanning Point Clouds. Geoscience & Remote Sensing Symposium, Beijing, 10-15 July 2016, 1595-1598.
https://doi.org/10.1109/IGARSS.2016.7729407
[8]  Hervieu, A. and Soheilian, B. (2013) Semi-Automatic Road/Pavement Modeling Using Mobile Laser Scanning. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Antalya, 12-13 November 2013, 31-36.
https://doi.org/10.5194/isprsannals-II-3-W3-31-2013
[9]  Yang, B., Dong, Z., Zhao, G., et al. (2015) Hierarchical Extraction of Urban Objects from Mobile Laser Scanning Data. ISPRS Journal of Photogrammetry and Remote Sensing, 99, 45-57.
https://doi.org/10.1016/j.isprsjprs.2014.10.005
[10]  Caltagirone, L., Scheidegger, S., Svensson, L., et al. (2017) Fast LIDAR-Based Road Detection Using Fully Convolutional Neural Networks. 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, 11-14 June 2017, 1019-1024.
https://doi.org/10.1109/ivs.2017.7995848
[11]  Fischler, M.A. and Bolles, R.C. (1981) Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24, 381-395.
https://doi.org/10.1145/358669.358692
[12]  Smadja, L., Ninot, J., Gavrilovic, T., et al. (2010) Road Extraction and Environment Interpretation from Lidar Sensors. IAPRS, Part 3A, Saint-Mandé, 1-3 September 2010, Vol. 38, 281-286.
[13]  Li, L., Yang, F., Zhu, H., et al. (2017) An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells. Remote Sensing, 9, 433.
https://doi.org/10.3390/rs9050433
[14]  Hough, V. and Paul, C. (1962) Method and Means for Recognizing Com-plex Patterns.
[15]  Hu, X., Li, Y., Shan, J., et al. (2014) Road Centerline Extraction in Complex Urban Scenes From LiDAR Data Based on Multiple Features. IEEE Transactions on Geoscience and Remote Sensing, 52, 7448-7456.
https://doi.org/10.1109/TGRS.2014.2312793
[16]  Habermann, D., Vido, C.E.O., Osorio, F.S., et al. (2016) Road Junction Detection from 3D Point Clouds. International Joint Conference on Neural Networks, Vancouver, 4934-4940.
https://doi.org/10.1109/IJCNN.2016.7727849
[17]  Fernandes, L.A.F. and Oliveira, M.M. (2008) Real-Time Line Detection through an Improved Hough Transform Voting Scheme. Pattern Recognition, 41, 299-314.
https://doi.org/10.1016/j.patcog.2007.04.003

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133