机载激光雷达测量系统及点云数据快速处理技术
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摘要
利用机载激光雷达测量系统可以直接、高效、全天候的获取高精度地面高程信息,所以机载激光雷达测量正成为一种最有效和最可靠的获取数字高程模型(DEM)的方法。机载激光雷达测量系统是集激光测距技术、惯性导航技术和差分GPS定位技术于一身的高科技空间信息测量系统,在国内外得到飞速的发展,并在许多行业中得到了广泛应用。然而相对于激光测量技术硬件的发展,数据量庞大呈离散分布的激光点云的相关数据处理技术还很滞后。特别是如何高效的对海量机载激光雷达点云数据组织管理和数据滤波分类并进行快速三维可视化渲染,已成为机载激光雷达测量技术中亟待解决的一个问题。
针对上述内容,本文首先对机载激光雷达测量系统中各项集成技术和设备进行研究,分析系统工作原理和激光雷达测量构像模型,并就系统数据处理中坐标系的定义、坐标系之间的转换模型、坐标转换矩阵等内容进行了研究。针对海量激光点云数据的可视化预处理问题,利用OpenGL三维渲染引擎结合Windows内核编程技术,编程实践激光点云数据快速读取和三维可视化渲染,实现激光点云数据的快速平滑三维浏览。引用GIS空间索引技术对海量激光点云数据进行组织管理,方便激光点云数据的快速搜索和三维空间信息的快速提取。对激光点云数据后处理的数据滤波问题,分析了几种高效的数据滤波技术,同时针对滤波计算计算量大的特点,提出并探讨利用CUDA并行运算构架进行数据滤波计算的可行性和数据处理过程。
Airborne LiDAR is becoming one of the most effective and reliable means for the digital elevation model (DEM) generation, because it can get the terrain elevation information directly, effectively and all-daytime with more precision. Airborne LiDAR is a high-technology system including LiDAR, INS and DGPS, which has been developed for decades at home and abroad and has been used widely by many departments. However, the technology of LiDAR data processing is not so progressive relative to the developing of the LiDAR device. Because the LiDAR data is so huge and disordered. How can we organized and filtering it effectively and visualize it quickly is a significant and urgent question.
This paper analyses the technology and devices composing the Airborne LiDAR system, and talks about the architectures of the Airborne LiDAR. Some coordinates are defined and the transformation between them is also introduced additionally. In order to meet the needs of the huge number LiDAR points-cloud's quick visualization, this paper discusses a method basing on OpenGL and Windows kernels programming skills to implement the points-cloud's visualization quickly and smoothly. The huge number LiDAR points-cloud is organized by the GIS spatial index structure so as to finding them effectively. The filtering of the LiDAR points-cloud is a significant step in the progress of LiDAR points-cloud's data processing. This paper also researches on some common methods for LiDAR data filtering with the parallel processors programming skills.
针对上述内容,本文首先对机载激光雷达测量系统中各项集成技术和设备进行研究,分析系统工作原理和激光雷达测量构像模型,并就系统数据处理中坐标系的定义、坐标系之间的转换模型、坐标转换矩阵等内容进行了研究。针对海量激光点云数据的可视化预处理问题,利用OpenGL三维渲染引擎结合Windows内核编程技术,编程实践激光点云数据快速读取和三维可视化渲染,实现激光点云数据的快速平滑三维浏览。引用GIS空间索引技术对海量激光点云数据进行组织管理,方便激光点云数据的快速搜索和三维空间信息的快速提取。对激光点云数据后处理的数据滤波问题,分析了几种高效的数据滤波技术,同时针对滤波计算计算量大的特点,提出并探讨利用CUDA并行运算构架进行数据滤波计算的可行性和数据处理过程。
Airborne LiDAR is becoming one of the most effective and reliable means for the digital elevation model (DEM) generation, because it can get the terrain elevation information directly, effectively and all-daytime with more precision. Airborne LiDAR is a high-technology system including LiDAR, INS and DGPS, which has been developed for decades at home and abroad and has been used widely by many departments. However, the technology of LiDAR data processing is not so progressive relative to the developing of the LiDAR device. Because the LiDAR data is so huge and disordered. How can we organized and filtering it effectively and visualize it quickly is a significant and urgent question.
This paper analyses the technology and devices composing the Airborne LiDAR system, and talks about the architectures of the Airborne LiDAR. Some coordinates are defined and the transformation between them is also introduced additionally. In order to meet the needs of the huge number LiDAR points-cloud's quick visualization, this paper discusses a method basing on OpenGL and Windows kernels programming skills to implement the points-cloud's visualization quickly and smoothly. The huge number LiDAR points-cloud is organized by the GIS spatial index structure so as to finding them effectively. The filtering of the LiDAR points-cloud is a significant step in the progress of LiDAR points-cloud's data processing. This paper also researches on some common methods for LiDAR data filtering with the parallel processors programming skills.
引文
1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,2010,12-16
2 Xiaoye Liu. Airborne LiDAR for DEM Generation:Some Critical Issues[J], Physical Geography,2008,32(1):31-49
3张小红.机载激光雷达测量技术理论与方法[M].武昌：武汉大学出版社,2007,49-53,105-107
4赖旭东.机载激光雷达基础原理与应用[M].北京：电子工业出版社,2010,146-149
5 Donald T.Knight. Rapid Development of Tightly-Coupled GPS/INS Systems[J], IEEE,1996, 4(96):300-305
6郭大海,吴立新等.IMU/DGPS辅助航空摄影新技术的应用[J],国土资源遥感,2006,1(67)：51-55
7李学友. IMU/DGPS辅助航空摄影测量原理、方法及实践[D],河南郑州：解放军信息工程大学,2005
8邓正隆.惯性技术[M].哈尔滨：哈尔滨工业大学出版社,2006,8-12,14-21
9郭素云.陀螺仪原理及应用[M].哈尔滨：哈尔滨工业大学出版社,1985,5-10
10陆元九.陀螺及惯性导航原理[M].北京：科学出版社,1964,54-60
11王勇.基于四元数描述的摄影测量定位理论研究[D],河南郑州：解放军信息工程大学,2007
12许佩珍,董长虹.四元数在战斗机飞行仿真中的应用[J]北京：.北京航空航天大学学报,1997,23(4)：487-491
13赖祖龙,万幼川等.基于Hilbert排列码与R树的海量LIDAR点云索引[J],测绘科学,2009,34(6)：128-131
14王永杰,孟令奎等.基于Hilbert空间排列码的海量空间数据划分算法研究[J],武汉大学学报,2007,32(7)：650-653
15李晨阳,段雄文等.N维Hilbert曲线生成算法[J],中国图像图形学报,2006,11(8)：1068-1075
16 Ibrahim Kamel,Christos Faloutsos. Hilbert R-Tree:An Improved R-tree Using Fractals[J]. ISR 1994,5(27):500-508
17 Mason Woo. OpenGL编程指南[M].北京：机械工业大学出版社,2008
18 Richard S.Wright Jr, Benjamin Lipchak. OpenGL SuperBible[M]. Boston:Pearson Education,2007,440-460
19 Jeffrey Richter. Windows核心编程[M].北京：清华大学出版社,2008,440-478
20 Johannes Kilian, Norbert Haala. Capture and evaluation of airborne laser scanner data[J]. International Archives of Photogrammetry and Remote Sensing,1996, XXXI(B3):383-386
21 Keqi Zhang, Shu-Ching Chen. A Progressive Morphological Filter for Removing Nonground Measurements From Airborne LIDAR Data[J]. IEEE,2003,41:40.
22 George Vosselman. Slope Based Filtering Of Laser Altimetry Data [J]. IAPRS,2000, ⅩⅩⅩⅢ,
23 K.Kraus,N.Pfeifer. Determination of terrain models in wooded areas with airborne laser scanner data [J]. ISPRS,1998,53:193-203.
24张小红,刘经南.机载激光扫描测高数据滤波[J],测绘科学,2004,29(6)：50-53
25刘艳丰.基于kd-tree的点云数据空间管理理论与方法[D],湖南长沙：中南大学,2009
26李清泉,李德仁.八叉树的三维行程编码[J],武汉测绘科技大学学报,1997,22(2)：102-106
27张舒,褚艳利.GPU高性能运算之CUDA[M].北京：中国水利水电出版社,2009,5-10
28 David B Kirk大规模并行处理器编程实战[M].北京：清华大学出版社2010,2-8,50-53
29王广运.载波相位差分GPS定位技术[J],测绘工程,1993,8(1)：13-17
30李清泉,李德仁.三维地理信息系统中的数据结构[J],武汉测绘科技大学学报,1996,21(2)：128-133
31张丽芬,王晓华等.基于网格划分的几种空间索引[J],北京：北京理工大学学报,2004,24(2)：141-144
32 Antonm Guttman. R-TREES A DYNAMIC INDEX STRUCTURE FOR SPATIAL SEARCHING.[J], ACM,1984,6(47):47-56
33阎超德,赵学胜.GIS空间索引方法评述[J],地理与地理信息科学,2004,20(4)：24-26
34郭晶,刘广军.基于空间网格和Hilbert R-Tree的二级R-Tree空间索引[J],北京：大学学报,2005,30(12)：1084-1088
35 Stanley B.Lippman, Josee Lajoie. C++ Primer中文版第四版[M].北京：人民邮电出版 社,2005
36 Erich Gamma,Richard Helm.设计模式[M].北京：机械工业出版社,2000
37孙鑫,余安萍.VC++深入详解[M].北京：电子工业出版社.2010
38李婕.三维海量激光数据管理及可视化研究[D],北京：首都师范大学,2009
39李军.三维GIS空间数据模型及可视化技术研究[D],湖南长沙,国防科学技术大学,2000
40张瑞菊.快速处理大数据量三维激光扫描数据的技术研究[J],测绘科学,2006,31(5)：93-95
41徐景中. LIDAR地面点云的简化方法研究[J],测绘信息与工程,2008,33(1)：32-34
42路兴昌.基于回波强度和采样点距离的点云滤波研究[J],测绘科学,2009,34(5)：196-197
2 Xiaoye Liu. Airborne LiDAR for DEM Generation:Some Critical Issues[J], Physical Geography,2008,32(1):31-49
3张小红.机载激光雷达测量技术理论与方法[M].武昌：武汉大学出版社,2007,49-53,105-107
4赖旭东.机载激光雷达基础原理与应用[M].北京：电子工业出版社,2010,146-149
5 Donald T.Knight. Rapid Development of Tightly-Coupled GPS/INS Systems[J], IEEE,1996, 4(96):300-305
6郭大海,吴立新等.IMU/DGPS辅助航空摄影新技术的应用[J],国土资源遥感,2006,1(67)：51-55
7李学友. IMU/DGPS辅助航空摄影测量原理、方法及实践[D],河南郑州：解放军信息工程大学,2005
8邓正隆.惯性技术[M].哈尔滨：哈尔滨工业大学出版社,2006,8-12,14-21
9郭素云.陀螺仪原理及应用[M].哈尔滨：哈尔滨工业大学出版社,1985,5-10
10陆元九.陀螺及惯性导航原理[M].北京：科学出版社,1964,54-60
11王勇.基于四元数描述的摄影测量定位理论研究[D],河南郑州：解放军信息工程大学,2007
12许佩珍,董长虹.四元数在战斗机飞行仿真中的应用[J]北京：.北京航空航天大学学报,1997,23(4)：487-491
13赖祖龙,万幼川等.基于Hilbert排列码与R树的海量LIDAR点云索引[J],测绘科学,2009,34(6)：128-131
14王永杰,孟令奎等.基于Hilbert空间排列码的海量空间数据划分算法研究[J],武汉大学学报,2007,32(7)：650-653
15李晨阳,段雄文等.N维Hilbert曲线生成算法[J],中国图像图形学报,2006,11(8)：1068-1075
16 Ibrahim Kamel,Christos Faloutsos. Hilbert R-Tree:An Improved R-tree Using Fractals[J]. ISR 1994,5(27):500-508
17 Mason Woo. OpenGL编程指南[M].北京：机械工业大学出版社,2008
18 Richard S.Wright Jr, Benjamin Lipchak. OpenGL SuperBible[M]. Boston:Pearson Education,2007,440-460
19 Jeffrey Richter. Windows核心编程[M].北京：清华大学出版社,2008,440-478
20 Johannes Kilian, Norbert Haala. Capture and evaluation of airborne laser scanner data[J]. International Archives of Photogrammetry and Remote Sensing,1996, XXXI(B3):383-386
21 Keqi Zhang, Shu-Ching Chen. A Progressive Morphological Filter for Removing Nonground Measurements From Airborne LIDAR Data[J]. IEEE,2003,41:40.
22 George Vosselman. Slope Based Filtering Of Laser Altimetry Data [J]. IAPRS,2000, ⅩⅩⅩⅢ,
23 K.Kraus,N.Pfeifer. Determination of terrain models in wooded areas with airborne laser scanner data [J]. ISPRS,1998,53:193-203.
24张小红,刘经南.机载激光扫描测高数据滤波[J],测绘科学,2004,29(6)：50-53
25刘艳丰.基于kd-tree的点云数据空间管理理论与方法[D],湖南长沙：中南大学,2009
26李清泉,李德仁.八叉树的三维行程编码[J],武汉测绘科技大学学报,1997,22(2)：102-106
27张舒,褚艳利.GPU高性能运算之CUDA[M].北京：中国水利水电出版社,2009,5-10
28 David B Kirk大规模并行处理器编程实战[M].北京：清华大学出版社2010,2-8,50-53
29王广运.载波相位差分GPS定位技术[J],测绘工程,1993,8(1)：13-17
30李清泉,李德仁.三维地理信息系统中的数据结构[J],武汉测绘科技大学学报,1996,21(2)：128-133
31张丽芬,王晓华等.基于网格划分的几种空间索引[J],北京：北京理工大学学报,2004,24(2)：141-144
32 Antonm Guttman. R-TREES A DYNAMIC INDEX STRUCTURE FOR SPATIAL SEARCHING.[J], ACM,1984,6(47):47-56
33阎超德,赵学胜.GIS空间索引方法评述[J],地理与地理信息科学,2004,20(4)：24-26
34郭晶,刘广军.基于空间网格和Hilbert R-Tree的二级R-Tree空间索引[J],北京：大学学报,2005,30(12)：1084-1088
35 Stanley B.Lippman, Josee Lajoie. C++ Primer中文版第四版[M].北京：人民邮电出版 社,2005
36 Erich Gamma,Richard Helm.设计模式[M].北京：机械工业出版社,2000
37孙鑫,余安萍.VC++深入详解[M].北京：电子工业出版社.2010
38李婕.三维海量激光数据管理及可视化研究[D],北京：首都师范大学,2009
39李军.三维GIS空间数据模型及可视化技术研究[D],湖南长沙,国防科学技术大学,2000
40张瑞菊.快速处理大数据量三维激光扫描数据的技术研究[J],测绘科学,2006,31(5)：93-95
41徐景中. LIDAR地面点云的简化方法研究[J],测绘信息与工程,2008,33(1)：32-34
42路兴昌.基于回波强度和采样点距离的点云滤波研究[J],测绘科学,2009,34(5)：196-197