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基于无人机点云的轨道检测与投影三维建模

Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud.

作者信息

Sahebdivani Shima, Arefi Hossein, Maboudi Mehdi

机构信息

School of Surveying and Geospatial Eng., College of Eng., University of Tehran, Tehran 1439957131, Iran.

Institute of Geodesy and Photogrammetry, Technische Universität Braunschweig, 38106 Braunschweig, Germany.

出版信息

Sensors (Basel). 2020 Sep 13;20(18):5220. doi: 10.3390/s20185220.

DOI:10.3390/s20185220
PMID:32933149
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7570914/
Abstract

The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway. In order to extract the railway lines, the height jump of points is evaluated in the neighborhood to select the candidate points of rail tracks. Then, using the RANSAC algorithm, line fitting on these candidate points is performed, and the final points related to the rail are identified. In the next step, the pre-specified rail piece model is fitted to the rail points through a projection-based process, and the orientation parameters of the model are determined. These parameters are later improved by fitting the Fourier curve, and finally a continuous 3D model for all of the rail tracks is created. The geometric distance of the final model from rail points is calculated in order to evaluate the modeling accuracy. Moreover, the performance of the proposed method is compared with another approach. A median distance of about 3 cm between the produced model and corresponding point cloud proves the high quality of the proposed 3D modeling algorithm in this study.

摘要

铁路行业的扩张增加了对铁轨三维建模的需求。由于无人机技术的不断发展及其应用优势,本研究利用无人机图像获取的密集点云对铁轨进行检测和三维建模。相应地,提出了一种基于铁轨整体方向的投影方法来生成铁路三维模型。为了提取铁路线,在邻域内评估点的高度跳跃以选择铁轨的候选点。然后,使用随机抽样一致性(RANSAC)算法对这些候选点进行直线拟合,并识别出与铁轨相关的最终点。下一步,通过基于投影的过程将预先指定的铁轨片段模型拟合到铁轨点上,并确定模型的方向参数。这些参数随后通过拟合傅里叶曲线得到改进,最终创建出所有铁轨的连续三维模型。计算最终模型与铁轨点之间的几何距离以评估建模精度。此外,将所提方法的性能与另一种方法进行了比较。所生成模型与相应点云之间约3厘米的中值距离证明了本研究中所提三维建模算法的高质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/e8506e2940ae/sensors-20-05220-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/8867548da500/sensors-20-05220-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/8c280706833f/sensors-20-05220-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/41341f21a3b0/sensors-20-05220-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/7b4c1ea0d011/sensors-20-05220-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/705da743bdcf/sensors-20-05220-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/f788110fdce4/sensors-20-05220-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/ca0c06015b38/sensors-20-05220-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/ec08ad3bcd3b/sensors-20-05220-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/44b0f99dc7b6/sensors-20-05220-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/e8506e2940ae/sensors-20-05220-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/8867548da500/sensors-20-05220-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/8c280706833f/sensors-20-05220-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/41341f21a3b0/sensors-20-05220-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/7b4c1ea0d011/sensors-20-05220-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/705da743bdcf/sensors-20-05220-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/f788110fdce4/sensors-20-05220-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/ca0c06015b38/sensors-20-05220-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/ec08ad3bcd3b/sensors-20-05220-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/44b0f99dc7b6/sensors-20-05220-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/7570914/e8506e2940ae/sensors-20-05220-g010.jpg

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本文引用的文献

1
Application of Template Matching for Improving Classification of Urban Railroad Point Clouds.模板匹配在改善城市铁路点云分类中的应用。
Sensors (Basel). 2016 Dec 12;16(12):2112. doi: 10.3390/s16122112.