车载测量系统数据处理若干关键技术研究
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摘要
本文以车载移动测量系统的应用为背景,重点探讨车载测量系统数据处理的理论、方法和关键技术,主要围绕POSLV实时组合导航策略、POS(GPS/IMU)数据后处理方法、车载测量传感器偏心分量的检校、序列影像的三维坐标解算与像点匹配、基于激光点云的电线杆和道路标线提取等方面进行了较为系统的分析和实验,取得了对工程应用有价值的实验结论。本文主要工作和创新点如下:
1.从车载测量组合传感器之间的空间位置和方位关系入手,探讨了各种车载传感器偏心分量采取各不相同的检校方法;提出了基于激光点云滤波分类的检校方法,实现了多组合传感器空间配准的精化。针对工程试验的需求,设计了车载测量系统的检校场与地面控制点的布设方案。
2.分析了GNSS/IMU/GAMS/DMI多种组合传感器系统POSLV的实时组合导航策略;并以两个工程实验测区获取的POS数据为基础,研究了单基准站紧组合差分SingleBase、单基准站松组合差分DGNSS、精密单点定位PPP等模式的POS数据后处理技术与方法,分析了GPS卫星无失锁和有失锁两种情况下POS数据后处理定位、定姿的精度。
3.将摄影测量空间前方交会的原理应用于车载序列影像的物方恢复,推导了车载序列影像空间前方交会计算地物点的公式,实现了基于SITCOL和SIOCTL两种方法的车载序列影像空间前方交会计算地物点的算法。
4.将计算机视觉理论引入到车载序列影像中,研究利用序列影像进行影像匹配获取同名像点的方法;实现了Harris算子的地物特征点提取,相关系数匹配,以及基于RANSAC稳健性估计的两视匹配和多视匹配,并将两视基本矩阵F、基于三视的三焦点张量T、相机矩阵的理论及方法应用于序列影像,实现了像点的匹配。
5.提出一种基于车载激光扫描数据的电线杆自动识别与定位算法。该算法将投影点密度的思想和电线杆的几何特性相结合,实现了电线杆的自动检测和定位,实验结果验证了该算法的可行性和有效性。
6.提出了一种基于激光LiDAR点云数据的道路标线自动识别算法。将激光LiDAR的回波反射率、扫描角、激光测量的距离与交通标线的属性特性相结合,实现了道路标线的自动识别,生成了道路标线的CAD图。
This dissertation presents a study of state-of-the-art Mobile Mapping System for Land Vehicle (MMS LV). The key techniques of data processings include strategies of real-time GPS-Aided Inertial Navigation Integration for POSLV, post-processing methods of POS (GPS/IMU) data, sensor calibration methods of lever arm and boresight misaligned angles, point matching based on sequence images in multiple views, calculating of three-dimensional coordinates based on Space Intersection, poles detection and road marking extraction from point clouds of LiDAR (Light Detection And Ranging). Based on the upper major aspects, a set of experiments and analysis have been made. Some useful conclusions which adapt to project application are educed. The followings are what we have done in this dissertation:
1. The major works focus on the space relationship of position and orientation among Digital Camera, Laser,GPS,DMI and IMU sensors, and then calibration methods of diverse sensor’s lever vector(lever arm and mounted angles) are discussed. A new calibration method is proposed to registration images with LiDAR’s point clouds which are partly filtered or classified, and multi-sensors integration spatial registration is refined. For the requirements of project tests, calibration sites of MMS and distribution scheme of ground control points are carefully designed.
2. According to data acquisition from two experimented projects, POS data post-processed techniques and methods, such as SingleBase, Differential GNSS, Precise Point Positioning (PPP) and so on, are investigated. Real-time navigation integration strategies of POSLV equipped with GPS/IMU/GAMS/DMI sensor systems are analyzed. Moreover, during GPS outages or without GPS outages,position and orientation accuracy about POS data post-processing is analyzed and compared.
3. By applying the photogrammetric theory of space intersection to sequence images obtained by land vehicle, calculation formula is educed to get the correct 3D coordinates of spatial point. Two space intersection algorithms, where one is Space Intersection of Two Camera and One Location (SITCOL) and another is Space Intersection of One Camera and Two Location (SIOCTL), are implemented.
4. The computer vision theory is introduced into homogeneous image points matching based on of sequence images in multi-views. Feature points detection by Harris operator, correlation coefficient matching and multiple views matching based on RANSAC solidity estimation are carried out through tests and analysis. Corresponding image points can be matched by two-view fundamental matrix (F), trifocal tensor (T) over three views, camera matrix and correlative theory or method in image sequence.
5. Considering the characteristics of point clouds collected by Vehicle-Borne Laser Scanning System, a new method is put forward on how to automatically identify and locate poles from laser scanning data. The Density of Projected Points and the geometrical quality of poles are taken into account to automatically identify and locate poles.And experimental results prove that the method is feasible and available.
6. An automatic road marking detection algorithm from LiDAR point clouds is proposed. Combining LiDAR features of retro, angle and distance with the properties of traffic marking, road marking is extracted, fitted and then used to generate CAD map.
1.从车载测量组合传感器之间的空间位置和方位关系入手,探讨了各种车载传感器偏心分量采取各不相同的检校方法;提出了基于激光点云滤波分类的检校方法,实现了多组合传感器空间配准的精化。针对工程试验的需求,设计了车载测量系统的检校场与地面控制点的布设方案。
2.分析了GNSS/IMU/GAMS/DMI多种组合传感器系统POSLV的实时组合导航策略;并以两个工程实验测区获取的POS数据为基础,研究了单基准站紧组合差分SingleBase、单基准站松组合差分DGNSS、精密单点定位PPP等模式的POS数据后处理技术与方法,分析了GPS卫星无失锁和有失锁两种情况下POS数据后处理定位、定姿的精度。
3.将摄影测量空间前方交会的原理应用于车载序列影像的物方恢复,推导了车载序列影像空间前方交会计算地物点的公式,实现了基于SITCOL和SIOCTL两种方法的车载序列影像空间前方交会计算地物点的算法。
4.将计算机视觉理论引入到车载序列影像中,研究利用序列影像进行影像匹配获取同名像点的方法;实现了Harris算子的地物特征点提取,相关系数匹配,以及基于RANSAC稳健性估计的两视匹配和多视匹配,并将两视基本矩阵F、基于三视的三焦点张量T、相机矩阵的理论及方法应用于序列影像,实现了像点的匹配。
5.提出一种基于车载激光扫描数据的电线杆自动识别与定位算法。该算法将投影点密度的思想和电线杆的几何特性相结合,实现了电线杆的自动检测和定位,实验结果验证了该算法的可行性和有效性。
6.提出了一种基于激光LiDAR点云数据的道路标线自动识别算法。将激光LiDAR的回波反射率、扫描角、激光测量的距离与交通标线的属性特性相结合,实现了道路标线的自动识别,生成了道路标线的CAD图。
This dissertation presents a study of state-of-the-art Mobile Mapping System for Land Vehicle (MMS LV). The key techniques of data processings include strategies of real-time GPS-Aided Inertial Navigation Integration for POSLV, post-processing methods of POS (GPS/IMU) data, sensor calibration methods of lever arm and boresight misaligned angles, point matching based on sequence images in multiple views, calculating of three-dimensional coordinates based on Space Intersection, poles detection and road marking extraction from point clouds of LiDAR (Light Detection And Ranging). Based on the upper major aspects, a set of experiments and analysis have been made. Some useful conclusions which adapt to project application are educed. The followings are what we have done in this dissertation:
1. The major works focus on the space relationship of position and orientation among Digital Camera, Laser,GPS,DMI and IMU sensors, and then calibration methods of diverse sensor’s lever vector(lever arm and mounted angles) are discussed. A new calibration method is proposed to registration images with LiDAR’s point clouds which are partly filtered or classified, and multi-sensors integration spatial registration is refined. For the requirements of project tests, calibration sites of MMS and distribution scheme of ground control points are carefully designed.
2. According to data acquisition from two experimented projects, POS data post-processed techniques and methods, such as SingleBase, Differential GNSS, Precise Point Positioning (PPP) and so on, are investigated. Real-time navigation integration strategies of POSLV equipped with GPS/IMU/GAMS/DMI sensor systems are analyzed. Moreover, during GPS outages or without GPS outages,position and orientation accuracy about POS data post-processing is analyzed and compared.
3. By applying the photogrammetric theory of space intersection to sequence images obtained by land vehicle, calculation formula is educed to get the correct 3D coordinates of spatial point. Two space intersection algorithms, where one is Space Intersection of Two Camera and One Location (SITCOL) and another is Space Intersection of One Camera and Two Location (SIOCTL), are implemented.
4. The computer vision theory is introduced into homogeneous image points matching based on of sequence images in multi-views. Feature points detection by Harris operator, correlation coefficient matching and multiple views matching based on RANSAC solidity estimation are carried out through tests and analysis. Corresponding image points can be matched by two-view fundamental matrix (F), trifocal tensor (T) over three views, camera matrix and correlative theory or method in image sequence.
5. Considering the characteristics of point clouds collected by Vehicle-Borne Laser Scanning System, a new method is put forward on how to automatically identify and locate poles from laser scanning data. The Density of Projected Points and the geometrical quality of poles are taken into account to automatically identify and locate poles.And experimental results prove that the method is feasible and available.
6. An automatic road marking detection algorithm from LiDAR point clouds is proposed. Combining LiDAR features of retro, angle and distance with the properties of traffic marking, road marking is extracted, fitted and then used to generate CAD map.
引文
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