基于车载LIDAR数据的建筑物立面重建技术研究
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摘要
车载激光扫描系统的出现为城市三维空间数据采集及建筑物立面模型的构建提供了一种全新的技术手段。它具有快速、精确、直接获取三维信息的特点,在城市三维建模中扮演着日益重要的角色。本文紧紧围绕基于车载LIDAR(Light Detection And Ranging)数据的建筑物立面重建这一主题,重点研究了车载LIDAR数据滤波、分割及建筑物立面特征提取等方面的内容,在理论和算法上取得了一定进展。本文完成的主要工作如下:
1.阐述了车载LIDAR系统的组成和对地定位原理,分析了点云数据特点及处理流程,详细介绍了现有的车载LIDAR点云滤波技术、建筑物立面点云分割方法及建筑物立面特征提取技术,归纳和分析了其中需要解决的问题。
2.在分析车载LIDAR数据沿扫描线分布空间特征的基础上,提出了一种基于扫描线的车载LIDAR数据滤波方法,并通过实验与基于投影点密度的滤波方法进行了对比分析,证明了该方法的效率和准确性。
3.实现了基于随机抽样一致性算法的建筑物立面点云分割。借鉴点云平面方程,引入计算机图形学中的r半径密度设计了相应的判断准则,给出了分割流程。通过分割实验,表明了该方法有利于分割效率的提高。
4.阐述了建筑物立面特征自动化识别策略,探讨了墙面和阳台的特征提取方法,提出了一种窗户特征提取的格网化方法,实现了从点云数据中提取出建筑物立面的二维平面特征和三维细节信息,进而自动地重建出具有较高细节层次的建筑物立面几何模型。
Vehicle-Borne Laser Scanning System provides a bran-new technical instrument for the three dimensional data collection of city modeling and reconstruction of building facade. It acts a more and more important role in three dimensional city modeling with the character of obtaining three dimensional information at first hand fleetly and accurately. This dissertation focuses on the theme of buiding facade reconstruction based on vehicle-borne LIDAR (Light Detection and Ranging) data closely, and puts keystone on the study of vehicle-borne LIDAR data filtering, segmtation, feature extraction and reconstruction of building facade, which gets along in theory and arithmetic to a certain extent. The major works implemented in this dissertation are listed as follow:
1.The components and position principle of vehicle-borne LIDAR system are expatiated. Then the existing methods of vehicle-borne LIDAR point cloud filtering, building facade point cloud segmentation, feature extraction and reconstruction of building facade are analyzed,the issues which need to be settled are summarized.
2.A vehicle-borne LIDAR data point cloud filtering method is put forward based on analyzing the distributing space characters of vehicle-borne LIDAR data along scanning beam. The contrast analysis are carried through filtering experiments compared with Density of Projected Points filtering method, and the efficiency and veracity of the method in this dissertation is proved.
3.Segmentation of building facade point cloud based on Random Sampling Consensus is implemented. The r-radicus density in Computer Graphics is introduced to devise segmentation judgement rule, and the segmentation flow is presented. Segmentation efficiency is improved, which is indicated in the segmentation experiments.
4.Automatic recognizing strategy is expatiated, and feature extraction method is discussed. A method of extracting window features by grids is brought forward. And two dimensional plain character and three dimensional detail information of building facade are extracted from point cloud data, and then building facade geometrical model of high detail levels is constructed automatically.
1.阐述了车载LIDAR系统的组成和对地定位原理,分析了点云数据特点及处理流程,详细介绍了现有的车载LIDAR点云滤波技术、建筑物立面点云分割方法及建筑物立面特征提取技术,归纳和分析了其中需要解决的问题。
2.在分析车载LIDAR数据沿扫描线分布空间特征的基础上,提出了一种基于扫描线的车载LIDAR数据滤波方法,并通过实验与基于投影点密度的滤波方法进行了对比分析,证明了该方法的效率和准确性。
3.实现了基于随机抽样一致性算法的建筑物立面点云分割。借鉴点云平面方程,引入计算机图形学中的r半径密度设计了相应的判断准则,给出了分割流程。通过分割实验,表明了该方法有利于分割效率的提高。
4.阐述了建筑物立面特征自动化识别策略,探讨了墙面和阳台的特征提取方法,提出了一种窗户特征提取的格网化方法,实现了从点云数据中提取出建筑物立面的二维平面特征和三维细节信息,进而自动地重建出具有较高细节层次的建筑物立面几何模型。
Vehicle-Borne Laser Scanning System provides a bran-new technical instrument for the three dimensional data collection of city modeling and reconstruction of building facade. It acts a more and more important role in three dimensional city modeling with the character of obtaining three dimensional information at first hand fleetly and accurately. This dissertation focuses on the theme of buiding facade reconstruction based on vehicle-borne LIDAR (Light Detection and Ranging) data closely, and puts keystone on the study of vehicle-borne LIDAR data filtering, segmtation, feature extraction and reconstruction of building facade, which gets along in theory and arithmetic to a certain extent. The major works implemented in this dissertation are listed as follow:
1.The components and position principle of vehicle-borne LIDAR system are expatiated. Then the existing methods of vehicle-borne LIDAR point cloud filtering, building facade point cloud segmentation, feature extraction and reconstruction of building facade are analyzed,the issues which need to be settled are summarized.
2.A vehicle-borne LIDAR data point cloud filtering method is put forward based on analyzing the distributing space characters of vehicle-borne LIDAR data along scanning beam. The contrast analysis are carried through filtering experiments compared with Density of Projected Points filtering method, and the efficiency and veracity of the method in this dissertation is proved.
3.Segmentation of building facade point cloud based on Random Sampling Consensus is implemented. The r-radicus density in Computer Graphics is introduced to devise segmentation judgement rule, and the segmentation flow is presented. Segmentation efficiency is improved, which is indicated in the segmentation experiments.
4.Automatic recognizing strategy is expatiated, and feature extraction method is discussed. A method of extracting window features by grids is brought forward. And two dimensional plain character and three dimensional detail information of building facade are extracted from point cloud data, and then building facade geometrical model of high detail levels is constructed automatically.
引文
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[2]康志忠.数字城市中街道景观主体三维可视化的快速实现研究[D].武汉:武汉大学,2004.
[3]张祖勋,张剑清.数字摄影测量学[M].武汉:武汉测绘科技大学出版社,1997.
[4]巩丹超,张永生.基于航空影像的建筑物半自动提取技术研究[J].测绘通报,2002:15-18.
[5]张祖勋,张剑清,胡翔云.基于物方空间几何约束最小二乘匹配的建筑物半自动提取方法[J].武汉大学学报(信息科学版),2001,26(4):290-295.
[6] Chon J,Fuse T,Shimizu E.Urban Visualization Through Video Mosaics Based on 3d Multi-base lines[A].In:Proceedings of XX th ISPRS Congress[C]., V01.XXXV,partB 3, 2004:727-731.
[7]李树楷,薛永祺.高效三维遥感集成技术系统[M].北京:科学出版社,2000.
[8]李然,王成等.星载激光雷达的发展与应用[J].科技导报,2007,25(14) :58-63.
[9]李清泉,李必军,陈静.激光雷达测量技术及其应用研究[J].武汉测绘科技大学学报, 2000,25(5):387-392.
[10]卢秀山,李清泉,冯文灏.车载式城市信息采集与三维建模系统[J].武汉大学学报(工学版),2003,36(3):76-80.
[11]李清泉.三维空间数据的实时获取,建模和可视化[M].武汉:武汉大学出版社,2003.
[12] Manandhar D,Shibasak R.Auto-extraction of urban features from vehicle–borne laser data[A].In: Symposium on Geospatial Theory[C].Processing and Applications,Ottawa 2002.
[13] Ackermann F.Airborne Laser Scanning-Present Status and Future Expectation[J].ISPRS Journal of Photogrammetry & Remote Sensing,1999,54: 64-67.
[14] Norbert Pfeifer, Christian Briesea. Geometrical aspects of airborne laser scanning and terrestrial laser scanning [A].In:IAPRS Volume XXXVI[C].Part 3 / W52, 2007.
[15] Manandhar D,Shibasak R.Vehicle-borne Laser Mapping System (VLMS) for 3-D Urban GIS Database[A].In:7th International Conference on Computers in Urban Planning and Urban Management Conf[C].CUPUM 2001.University of Hawaii at Manna. USA, CD-ROM publication.2001,July 18-20.
[16] Huijing Zhao,Ryosuke Shibasaki.Reconstructing textured CAD model of urban environment using vehicle-borne laser range scanners and line camera[A].In:Lecture Notes In computer Science [C].International Work ship on Computer Vision Systems Proceedings,2001:284-297.
[17] Frueh C , Zakhor A.Data Processing Algorithms for Generating Textured 3D Building Facade Meshes From Laser Scans and Camera Images[J].Proc 3D Processing Visualization and Transmission. IEEE CS Press.2002:834-847.
[18] http://www.Geo-T3D.com.
[19] http://www.applanix.com.
[20] http://www.3dlasermapping.com.
[21] Kukkoa.A Road environment mapping system of the Finnish Geodetic Institute: FGI Roamer [J].IAPRS, Vol. 36(3/W52), 2007:241-247.
[22] Http://www.Optech.com.
[23] Sequeira V, Ng K C, Butterfield S, Goncalves J G M, Hogg D C.Three dimensional textured models of indoor scenes from composite range and video images [A].In:Three-Dimensional Image Capture and Applications[C].Proceedings of SPIE, 1998.
[24] Stamos.I,Allen P K.Geometry and texture recovery of scenes of large scale[J].Computer Vision and Image Understanding,2002(88):94-118.
[25] http://www.isprs.org/proceedings/XXXVII/congress/tc3a.aspx.
[26]李必军,方志祥,任娟.从激光扫描数据中进行建筑物特征提取研究[J].武汉大学学报(信息科学版).2003,28(1):65-70.
[27]史文中,李必军,李清泉.基于投影点密度的车载激光扫描距离图像分割方法[J].测绘学报.2002,32(2):95-100.
[28]黄磊.基于激光扫描数据的建筑物立面信息提取[D].山东科技大学硕士论文,2002:25-33.
[29]李庆田.车载GPS公路测量中数据采集与数据处理方法研究[D].解放军信息工程大学测绘学院硕士论文,2006.
[30] Jan B?hm.Facade detail from incomplete range data [J].ISPRS,XXXIII, Commission V, WG V/4,2008:653-658.
[31]王清毅,蔡智,邹翔.部分数据缺失环境下的知识发现方法[J].软件学报,2001,12(10):1516-1524.
[32]冯文灏,侯文广,张纯连.一种获取大比例尺建筑物立面影像的方法[J].武汉大学学报(信息科学版).2005,30(8):673-676.
[33]张小红.机载激光扫描测高数据滤波及地物提取[D].武汉大学博士论文,2002.
[34]刘春,陈华云等.激光三维遥感的数据处理与特征提取[M].北京:科学出版社,2009:37-40.
[35] Kraus K, Pfeifer N.Determination of terrain models in wood areas with airborne laser scanner data[J].ISPRS J. Photogram. Remote Sensing, 1998(53):193–203.
[36] Rottensteiner F.Semi-automatic extraction of buildings based on hybrid adjustment using 3D surface models and management of building data in a TIS[D].TU Wien.2001.
[37] Zhang Ke qi.A Progressive Morphological Filter for Removing Non ground Measurements From Airborne LIDAR Data[J].IEEE Transactions on Geo-science and Remote Sensing, 2003,41(4):872-882.
[38] Sithole G, Vosselman G.Experimental comparison of filter algorithms for bare-earth extraction from airborne laser point clouds[J].ISPRS Journal of Photogrammetry and Remote Sensing.2004,59(12):85-101.
[39] Axelsson P.DEM Generation from Laser Scanner Data Using Adaptive TIN Models [J]. IAPRS, 2000, 33:110-117.
[40] Brovelli M A.Modelli matematici del terreno per mezzo de interpolatori a spline. Bollettino SIFET[J].Supplement special al n. 2, 2000:55-80.
[41] Besel P J, Jain R C.Segmentation through variable-order surface fitting[J].IEEE Transactions on pattern Analysis and Machine Intelligence ,1988,10(2):167-192.
[42] Rudolph Triebel,Kristian Kersting,Wlofram Burgard.Robust 3D Scan Point Classification usingAssociative Markov Networks[A] . In : Robotics and automation ICRA Processing[C] . IEEE International conference,2006.
[43] Belton D,Lichti D.Classification and segmentation of terrestrial laser scanner point clouds using local variance Information. In: The International Archives of the Photogrammetry[C].Remote Sensing and Spatial Information Sciences, Dresden, Germany, Vol. XXXVI, Part 5, 2006:44-49.
[44] Vosselman G,Gorte B G H,Sithole G,Rabbani T.Recognizing Structure in Laser Scanner Point Clouds, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences[J], vol. 46, part 8/W2, Freiburg, Germany ,October 4-6, 2004:33-38.
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