基于LiDAR点云和航空影像的城市三维重建
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摘要
随着GIS的迅速发展和数字城市的快速建模的需要,对城市环境中各种三维信息的表达与处理变得日益迫切。近几年LiDAR(Light Detecting AndRanging)技术的出现,为解决这一问题提供了新的途径。机载LiDAR获取的数据是分布于对象表面的三维点坐标,其数据集(点云)是对象的数字表面模型(DSM)。由于机载LiDAR能够自动地获取高精度、高密度的地球表面3D坐标信息,已成为生成数字地面模型(DTM)和数字高程模型(DEM)的首选工具。
针对中、低密度的机载LiDAR数据和航空影像的城市三维重建工作,本文主要从以下几个方面开展研究:
(1)机载LiDAR技术的发展使得对大规模地表非均匀点云构网成为研究的热点,本文在Delaunay三角网生长法的基础上改进,提出一种新的三角网生长算法。该算法先对大规模点云进行等格网分块,然后自适应确定搜索范围。构建过程中对生成的基线分组、排序,动态删除封闭点,提高了构建三角网的速度;在整个点集范围内进行搜索,避免了插值产生的误差和模块之间的拼接。最后对大规模LiDAR点云数据构网,表明了该算法的有效性。
(2)根据城市地形变化缓慢的特性,在三维Hough变换的基础上添加约束条件,快速生成地形的基准平面,然后根据点云TIN网格与基准平面的位置关系以及TIN三角形法线的方向提取地面点。为保持场景地形的边界,本文提出一种新的二维点云凸包生成算法,首先对点云进行分块和在多方向生成最远点而得到初始凸包,并在凸包交界的局部区域内搜索最优点来扩展初始凸包,从而提取LiDAR点云凸包。最后结合提取的地面点和场景的凸包生成最终的地形TIN模型,该方法对点集的密度要求低,提取城市地形的鲁棒性高。
(3)从航空或卫星影像中提取道路一直是研究的热点,基于动态规划的道路提取算法是最有效的算法之一。本文基于LiDAR点云数据特征改进了该算法的代价函数,进而提高了基于动态规划的道路提取算法的鲁棒性。为正确地融合航拍图像和LiDAR点云数据,本文提出了一种航拍图像和LiDAR点云数据的匹配算法。最后通过试验验证了算法的正确性。
With the rapid development of GIS and the necessity of fast modeling of digital cities, it is eager to express and deal with various 3D information in the cities. In recent years, the emergence of LiDAR provides a new way to solve the problem. The data obtained by LiDAR are 3D coordinates of points on the earth surface, which express the Digital Surface Model. LiDAR has become the preferred tool to generate Digital Terrain Model and Digital Elevation Model because it can obtain high-definition and high-density 3D coordinates of earth surface automatically.
According to the medium and low density LiDAR and aerial images, several aspects about urban 3D reconstruction are studied as follows:
(1) With the development of LiDAR, building the TIN model with the non-uniform point-clouds of the terrain surface becomes a hot research field. Based oh the existing Delaunay triangulation method, a new algorithm of triangulation growth is presented in this dissertation. The algorithm divides the large-scale point clouds into uniform grids and determines the searching scope self-adaptively. During the process of building a Triangulated Irregular Network (TIN) model, the generated base-lines are grouped and the close-points are removed dynamically, which can improve the speed of reconstructing TIN in large-scale scenes dramatically. By searching the triangular vertices in the scope of the whole data set, the method can avoid errors caused by interpolation and the process of stitching between grids. The efficiency and effectiveness of the algorithm are verified by using real world data to build TIN model with large scale LiDAR point clouds.
(2) According to the traits of urban slowly-changing terrain, based on the 3D Hough transform, restricted conditions are imposed in the dissertation, which firstly builds the basic plane of the terrain, and then extract the points on the ground in term of the position relation between the point-cloud TIN and the basic plane, and of the direction of the normal of the TIN. In order to preserve the boundary points, a new algorithm of building 2D convex hull is discussed. Firstly the points are divides into grids and the initialized convex hull is gained by seeking for the farthermost points in varied directions, then the initialized convex hull is expanded iteratively by searching the best points in local common boundary area, and finally the full convex hull from the LiDAR point-cloud can be obtained. The algorithm, which builds the final terrain TIN model by combining the points on the ground and the points of the convex hull, has low need of density of the point-cloud but has high robustness in the extraction of urban terrain.
(3) The road extraction from aerial images is always a research hotspot, and the algorithm of road extraction based on dynamic programming is one of the most efficient algorithms, which is improved in this dissertation based on LiDAR point-cloud, and the robustness is also inhanced. In order to combine the aerial image and the LiDAR point-cloud data, the algorithm of matching the aerial image with the LiDAR point-cloud data is put forward. In the end, experiments are carried out to validate the algorithm.
针对中、低密度的机载LiDAR数据和航空影像的城市三维重建工作,本文主要从以下几个方面开展研究:
(1)机载LiDAR技术的发展使得对大规模地表非均匀点云构网成为研究的热点,本文在Delaunay三角网生长法的基础上改进,提出一种新的三角网生长算法。该算法先对大规模点云进行等格网分块,然后自适应确定搜索范围。构建过程中对生成的基线分组、排序,动态删除封闭点,提高了构建三角网的速度;在整个点集范围内进行搜索,避免了插值产生的误差和模块之间的拼接。最后对大规模LiDAR点云数据构网,表明了该算法的有效性。
(2)根据城市地形变化缓慢的特性,在三维Hough变换的基础上添加约束条件,快速生成地形的基准平面,然后根据点云TIN网格与基准平面的位置关系以及TIN三角形法线的方向提取地面点。为保持场景地形的边界,本文提出一种新的二维点云凸包生成算法,首先对点云进行分块和在多方向生成最远点而得到初始凸包,并在凸包交界的局部区域内搜索最优点来扩展初始凸包,从而提取LiDAR点云凸包。最后结合提取的地面点和场景的凸包生成最终的地形TIN模型,该方法对点集的密度要求低,提取城市地形的鲁棒性高。
(3)从航空或卫星影像中提取道路一直是研究的热点,基于动态规划的道路提取算法是最有效的算法之一。本文基于LiDAR点云数据特征改进了该算法的代价函数,进而提高了基于动态规划的道路提取算法的鲁棒性。为正确地融合航拍图像和LiDAR点云数据,本文提出了一种航拍图像和LiDAR点云数据的匹配算法。最后通过试验验证了算法的正确性。
With the rapid development of GIS and the necessity of fast modeling of digital cities, it is eager to express and deal with various 3D information in the cities. In recent years, the emergence of LiDAR provides a new way to solve the problem. The data obtained by LiDAR are 3D coordinates of points on the earth surface, which express the Digital Surface Model. LiDAR has become the preferred tool to generate Digital Terrain Model and Digital Elevation Model because it can obtain high-definition and high-density 3D coordinates of earth surface automatically.
According to the medium and low density LiDAR and aerial images, several aspects about urban 3D reconstruction are studied as follows:
(1) With the development of LiDAR, building the TIN model with the non-uniform point-clouds of the terrain surface becomes a hot research field. Based oh the existing Delaunay triangulation method, a new algorithm of triangulation growth is presented in this dissertation. The algorithm divides the large-scale point clouds into uniform grids and determines the searching scope self-adaptively. During the process of building a Triangulated Irregular Network (TIN) model, the generated base-lines are grouped and the close-points are removed dynamically, which can improve the speed of reconstructing TIN in large-scale scenes dramatically. By searching the triangular vertices in the scope of the whole data set, the method can avoid errors caused by interpolation and the process of stitching between grids. The efficiency and effectiveness of the algorithm are verified by using real world data to build TIN model with large scale LiDAR point clouds.
(2) According to the traits of urban slowly-changing terrain, based on the 3D Hough transform, restricted conditions are imposed in the dissertation, which firstly builds the basic plane of the terrain, and then extract the points on the ground in term of the position relation between the point-cloud TIN and the basic plane, and of the direction of the normal of the TIN. In order to preserve the boundary points, a new algorithm of building 2D convex hull is discussed. Firstly the points are divides into grids and the initialized convex hull is gained by seeking for the farthermost points in varied directions, then the initialized convex hull is expanded iteratively by searching the best points in local common boundary area, and finally the full convex hull from the LiDAR point-cloud can be obtained. The algorithm, which builds the final terrain TIN model by combining the points on the ground and the points of the convex hull, has low need of density of the point-cloud but has high robustness in the extraction of urban terrain.
(3) The road extraction from aerial images is always a research hotspot, and the algorithm of road extraction based on dynamic programming is one of the most efficient algorithms, which is improved in this dissertation based on LiDAR point-cloud, and the robustness is also inhanced. In order to combine the aerial image and the LiDAR point-cloud data, the algorithm of matching the aerial image with the LiDAR point-cloud data is put forward. In the end, experiments are carried out to validate the algorithm.
引文
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[28]Pos A.,Vale G..Dynamic programming approach for semi- automated road extraction from medium and high-resolution images[J].International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2003,34(8):87-91
[29]Armin Gruen,Haihong Li.Semi-automatic linear feature extraction by dynamic programming and LSB-snakes[J].Photogrammetric Engineering and Romote Sensing,1997,63(8):985-995
[30]Shackelford A.K.,Davis C.H..Fully automated road network extraction from high-resolution satellite multispectral imagery[C].PPProceedings of the IEEE International Geoscience and Remote Sensing Symposium,2003,Vol(1): 461-463
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[2]张昊.LIDAR建筑物提取方法研究[硕士学位论文].天津:中国民航大学.2007.
[3]Elmqvist M,Jungert E,Lantz F,et al.Terrain Modelling and Analysis Using Laser Scanner Data[C].IAPRS,Annapolis,MD,2001
[4]Roggero M,Airborne Laser Scanning:Clustering in Raw Data[C].IAPRS,Annapolis,2001
[5]Wack R,Wimmer A.Digital Terrain Models from Airborne Laser Scanner Data:a Grid Based Approach[C].IAPRS,Graz,Austria,2002
[6]Sithle G.Filtering of Laser Altimetry Data Using a Slope Adaptive Filter[C].IAPRS,Annapolis,MD,2001.
[7]Sohn G,Dowman I.Terrain Surface Reconstruction by the Use of Tetrahedron Model with the MDL Criterion[C].IAPRS,Graz,Austria,2002
[8]Axelsson P.DEM Generation from Laser Scanner Data Using Adaptive TIN Models[C].IAPRS,Amsterdam,2000
[9]Sithole G,Vosselman G.Experimental Comparison of Filter Algorithms for Bare-Earth Extraction from Airborne Laser Scanning Points Clouds[J].ISPRS Journal of Photogrammetry & Remote Sensing,2004,59:85-101
[10]孔德慧,马春玲.一种平面点集凸包与三角网综合生成的算法[J].计算机研究与发展,2000,37(7):891-896
[11]Kallay M.The Complexity of Incremental Convex Hull Algorithm in Rd[J].Information Processing Letters,1984,(19):197
[12]Graham R L.An Efficient Algorithm for Determining the Convex Hull of a Finite Point Set[J].Information Processing Letters,1972,(1):132-133
[13]Javis R A.On the Identification of the Convex Hull of a Finite Set of Points in the Plane[J].Information Processing Letters,1973,(2):18-21
[14]Miller R,Strut Q F.Mesh Computer Algorithms for Computational Geometry[J].IEEE Trans Computer,1989,38(3):321-340
[15]刘新,刘任任.一种改进的构建凸包的分治算法[J].计算机工程与科学,2006,28(8):63-65
[16]Zhou Qihai,Huang Tao,Wu Hongyu,Li Zhongjun,Lin Xun.A New Algorithm for Finding Convex Hull with a Maximum Pitch of the Dynamical Base Line[J].IEEE:2008 Workshop on Knowledge Discovery and Data Mining,2008: 630-634
[17]王志强,洪嘉振,肖立谨.平面点集凸包的最优实时算法[J].计算机学报,1998,21(增刊):351-356
[18]蒋红菲.平面点集凸包快速构建算法的研究[J].计算机工程与应用,2002,20:48-49
[19]余翔宇,孙洪,余志雄.改进的二维点集凸包快速求取方法[J].武汉理工大学学报,2005,27(10):81-83,92
[20]郝建强.利用正负划分性求平面点集凸包的最优算法[J].中国图象图形学报,2007,12(5):910-916
[21]Chadnov R V,Skvortsov A V.Convex Hull Algorithm Review[J].IEEE:Science and Technology,KORUS 2004,Proceedings,2004:112-115
[22]Barber C B,Dobkin D P,Huhdanpaa H.The Quickhull Algorthm for Convex Hulls[J].ACM Transaction on Mathematical Software,1996,22(4):469-483
[23]金文华,何涛,刘晓平,唐卫清,唐荣锡.基于有序简单多边形的平面点集凸包快速求取算法[J].计算机学报,1998,21(6):533-539
[24]Ton J.,Jain A.K.,Enslin W.R.,et al.Automatic road identification and labeling in Landsat 4 TM images[J].Photogrammetric Engineering & Remote Sensing,1989,43(2):57-276
[25]Wang F.,Newkirk R..A knowledge- based system for highway network extraction[J].IEEE Transactions on Geoscience and Remote Sensing,1988,26(5):525-531
[26]Barzohar M.,Cooper D.B..Automatic finding of main roads in aerial images by using geometric- stochastic models and estimation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(7):707-721
[27]杨云,朱长青,张德.高分辨率遥感影像上道路中心线的半自动提取[J].计算机辅助设计与图形学学报,2007,19(6):781-785
[28]Pos A.,Vale G..Dynamic programming approach for semi- automated road extraction from medium and high-resolution images[J].International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2003,34(8):87-91
[29]Armin Gruen,Haihong Li.Semi-automatic linear feature extraction by dynamic programming and LSB-snakes[J].Photogrammetric Engineering and Romote Sensing,1997,63(8):985-995
[30]Shackelford A.K.,Davis C.H..Fully automated road network extraction from high-resolution satellite multispectral imagery[C].PPProceedings of the IEEE International Geoscience and Remote Sensing Symposium,2003,Vol(1): 461-463
[31]Zhu C.,Shi W.,Pesaresi M.,et al.The recognition of road network from high-resolution satellite remotely sensed data using image morphological characteristics[J].International Journal of Remote Sensing,2005,26(24):5493-5508
[32]Hatger C.,Brenner C..Extraction of road geometry parameters form laser scanning and existing databases[J].Int'l Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,Vol.ⅩⅩⅣ,Part 3/W13,2003
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