车载MMS激光点云与序列全景影像自动配准方法

提出一种车载移动测量系统（MMS）激光点云与序列全景影像自动配准方法。首先采用层次化城市场景目标提取方法自激光点云提取天际线矢量,在全景影像中经虚拟成像与分割角点提取算法生成天际线矢量。然后,将提取结果作为几何配准基元,构建配准基元图,通过最小化配准基元图编辑距离进行匹配,组成共轭配准基元对,解算2D-3D粗配准模型,获得全景影像与LiDAR点云参考坐标系之间的初始转换关系。最后,为消除几何配准基元提取与匹配误差对配准结果的影响,自序列全景影像虚拟成像影像生成多视立体密集匹配点云,继而使用变种ICP算法优化其与激光点云数据间3D-3D配准参数,间接优化全景影像与激光点云间的配准参数,精化配准结果。试验结果表明,本文提出的自动配准方法可以实现车载MMS激光点云与序列全景影像的1.5像素级自动配准,配准成果可应用于真彩色点云生成等点云/影像数据融合应用。     

基于二维图像特征的点云配准方法

为了提高低覆盖率点云的配准精度和收敛速度,提出了一种基于二维图像特征的点云配准方法。首先采用基于区域层次的点云配准算法实现粗配准;然后将三维点云转换成二维图像,再采用SURF算法提取二维图像的特征,并求解其匹配像素点对;最后根据二维匹配点获取相应的三维点云相关点,并计算刚体变换,由此实现点云的快速精确配准。试验结果表明,与迭代最近点（ICP）算法相比,该点云配准方法的配准精度和耗时分别提高了约20%和60%,是一种快速、高精度的点云配准算法。     

An integrated approach for modelling and global registration of point clouds

Point cloud acquisition by using laser scanners provides an efficient way for 3D as-built modelling of industrial installations. Covering such an installation with point cloud data often requires data acquisition from multiple standpoints. Before the actual modelling can start the transformation parameters of all scans need to be determined. Two methods to register point clouds of industrial scenes with different coordinate definitions are presented. Corresponding object models in different scans are used to determine the translation and rotation parameters of the scans. The first method, called Indirect method, is a two-step approach as object fitting and registration of the scenes is done separately. The second method, called Direct method simultaneously determines the shape and pose parameters of the objects as well as the registration parameters. Both methods are designed such that optimal use can be made of the knowledge of shapes present in industrial environments. Compared to ICP the presented approach combines registration and modelling and thus avoids the accumulation of errors. Furthermore, the simultaneous registration of multiple scans is possible. The presented approaches are based on non-linear least squares and provide quality measures in the form of covariance matrix of the estimated parameters, which can be used to decide if more scans are needed, and how and where they should be captured. Results are presented on some point cloud data-sets from actual industrial sites, where registration was done without using any artificial targets.     

地形三维建模中特殊地物的处理方法

在三维建模领域,空中三角测量通过少量的控制点,按一定的数学模型加密解算出所需要的全部控制点及每张像片的外方位元素并由此进行三维重建工作;若遇到一些特殊地物(如桥梁、铁塔、水域等)时,往往会出现模型错乱、模糊不清、有漏洞或无法正确显示等一系列问题。本文采用倾斜摄影测量建模的方式,对一些特殊地物进行信息采集,再利用专业软件进行加工处理,用3dsmax类建模软件对采集的信息进行精细建模,用skyline类软件进行三维场景编辑及漫游,从而能够实现基于原本样貌重建特殊地物的三维模型。     

一种新型三维激光扫描隧道测量点云坐标定位方法的精度评估

提出了一种基于三维激光扫描仪特殊定位硬件装置和七参数坐标转换算法,对每个扫描测站点云利用最近的隧道控制点进行绝对定位,将多个隧道扫描测站数据不经过拼接就统一转换成隧道控制测量坐标系坐标的方法——点云绝对定位法,并应用误差传播理论和相应数值模拟计算对该方法的测量成果进行精度评估和重要误差来源分析。最后得出在一定条件下采用该方法获得的隧道扫描测量成果可以满足城市地铁测量规范对竣工测量精度要求的结论。     

Keypoint-based 4-Points Congruent Sets – Automated marker-less registration of laser scans

We propose a method to automatically register two point clouds acquired with a terrestrial laser scanner without placing any markers in the scene. What makes this task challenging are the strongly varying point densities caused by the line-of-sight measurement principle, and the huge amount of data. The first property leads to low point densities in potential overlap areas with scans taken from different viewpoints while the latter calls for highly efficient methods in terms of runtime and memory requirements.A crucial yet largely unsolved step is the initial coarse alignment of two scans without any simplifying assumptions, that is, point clouds are given in arbitrary local coordinates and no knowledge about their relative orientation is available. Once coarse alignment has been solved, scans can easily be fine-registered with standard methods like least-squares surface or Iterative Closest Point matching. In order to drastically thin out the original point clouds while retaining characteristic features, we resort to extracting 3D keypoints. Such clouds of keypoints, which can be viewed as a sparse but nevertheless discriminative representation of the original scans, are then used as input to a very efficient matching method originally developed in computer graphics, called 4-Points Congruent Sets (4PCS) algorithm. We adapt the 4PCS matching approach to better suit the characteristics of laser scans.The resulting Keypoint-based 4-Points Congruent Sets (K-4PCS) method is extensively evaluated on challenging indoor and outdoor scans. Beyond the evaluation on real terrestrial laser scans, we also perform experiments with simulated indoor scenes, paying particular attention to the sensitivity of the approach with respect to highly symmetric scenes.     

Automated matching of multiple terrestrial laser scans for stem mapping without the use of artificial references

Terrestrial laser scanning has been widely used to analyze the 3D structure of a forest in detail and to generate data at the level of a reference plot for forest inventories without destructive measurements. Multi-scan terrestrial laser scanning is more commonly applied to collect plot-level data so that all of the stems can be detected and analyzed. However, it is necessary to match the point clouds of multiple scans to yield a point cloud with automated processing. Mismatches between datasets will lead to errors during the processing of multi-scan data. Classic registration methods based on flat surfaces cannot be directly applied in forest environments; therefore, artificial reference objects have conventionally been used to assist with scan matching. The use of artificial references requires additional labor and expertise, as well as greatly increasing the cost. In this study, we present an automated processing method for plot-level stem mapping that matches multiple scans without artificial references. In contrast to previous studies, the registration method developed in this study exploits the natural geometric characteristics among a set of tree stems in a plot and combines the point clouds of multiple scans into a unified coordinate system. Integrating multiple scans improves the overall performance of stem mapping in terms of the correctness of tree detection, as well as the bias and the root-mean-square errors of forest attributes such as diameter at breast height and tree height. In addition, the automated processing method makes stem mapping more reliable and consistent among plots, reduces the costs associated with plot-based stem mapping, and enhances the efficiency.     

摄影地形测量数据的三维融合和可视化

本文首先解释T-共线方程的概念,提出了基于T-共线方程进行三维配准的准则和数学模型,以及两共轭影像在三维空间进行配准和三维重建的计算方法。可视化将人类对现实世界认识的准则:感觉-思维-认知,应用于对数据世界的认识,因此配准、融合和立体视觉重建,有助于对地球影像的理解和解译。本文以三个案例的卫星和航空摄影地形测量数据,基于T-共线方程,对摄影地形测量数据的配准、融合和可视化,进行了原理性试验。     

广州“数字详规”项目空间数据生产与组织的若干问题探讨

本文讨论了三维空间信息系统下有关数据的生产作业方式,以及一种基于Skyline软件体系的空间数据组织方式,将生产出来的数据以一种有效、合理的方式进行管理与调度,使系统与数据适配和谐,为系统应用打下良好的基础。     

Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge

Automatic 3D point cloud registration is a main issue in computer vision and remote sensing. One of the most commonly adopted solution is the well-known Iterative Closest Point (ICP) algorithm. This standard approach performs a fine registration of two overlapping point clouds by iteratively estimating the transformation parameters, assuming good a priori alignment is provided. A large body of literature has proposed many variations in order to improve each step of the process (namely selecting, matching, rejecting, weighting and minimizing). The aim of this paper is to demonstrate how the knowledge of the shape that best fits the local geometry of each 3D point neighborhood can improve the speed and the accuracy of each of these steps. First we present the geometrical features that form the basis of this work. These low-level attributes indeed describe the neighborhood shape around each 3D point. They allow to retrieve the optimal size to analyze the neighborhoods at various scales as well as the privileged local dimension (linear, planar, or volumetric). Several variations of each step of the ICP process are then proposed and analyzed by introducing these features. Such variants are compared on real datasets with the original algorithm in order to retrieve the most efficient algorithm for the whole process. Therefore, the method is successfully applied to various 3D lidar point clouds from airborne, terrestrial, and mobile mapping systems. Improvement for two ICP steps has been noted, and we conclude that our features may not be relevant for very dissimilar object samplings.     

基于Skyline和IDL平台的地质数据三维可视化研究

地上地下信息三维可视化在地质勘探中具有重要作用。首先研究了地质数据建模的主要方法;在分析和比较当前主要三维可视化平台的基础上,基于Skyline和IDL开发平台进行了详细的系统设计;最后,开发和实现了集地上地形地貌信息和地下地质数据的三维可视化分析系统。结果表明,文中的设计方案能够很好地集成地质勘探中的地上地下信息,实现三维可视化分析。     

基于Skyline的三维城市模型数据库管理系统设计与实现

城市三维模型在城市规划、建设等方面得到了广泛的应用。本文介绍了城市三维模型管理的现状,分析了三维城市模型建库管理的需求,基于Skyline三维地理信息系统和ArcGIS Engine,设计了三维模型数据库的存储结构,并开发了三维模型数据库管理系统,实现了三维模型的建库管理。     

Automated Image Registration for Hydrologic Change Detection in the Lake-Rich Arctic

Multitemporal remote sensing provides a unique tool to track lake dynamics at the pan-Arctic scale but requires precise registration of thousands of satellite images. This is a challenging task owing to a dearth of stable features to be used as tie points [(TPs), i.e., control points] in the dynamic landscapes. This letter develops an automated method to precisely register images in the lake-rich Arctic. The core premise of the method is that the centers of lakes are generally stable even if their shorelines are not. The proposed procedures first extract lakes in multitemporal satellite images, derive lake centroids and match them between images, and then use the centroids of stable lakes as TPs for image registration. The results show that this approach can achieve subpixel registration accuracy, outcompeting the conventional manual methods in both efficiency and accuracy. The proposed method is fully automated and represents a feasible way to register images for lake change detection at the pan-Arctic scale.      

多视激光点云数据融合与三维建模方法研究

基于特征基元的点云数据配准方法,利用控制点对机载与车载点云数据进行概略匹配,构建了顾及梯度与颜色特征及特征组对的特征点匹配算法模型,根据拟合平面特征解算平移和旋转变换参数,实现了机载与车载点云数据的精确配准,并在此基础上建立了多角度点云数据融合的房屋顶部和立面特征提取、点云数据与光学影像纹理信息匹配的技术流程,实现了建（构）筑物三维精细建模,并通过实例验证了本文所提方法的有效性。     

一种基于角点特征的遥感影像自动配准方法

本文通过对Harris算子进行改进,自适应地提取角点特征,然后基于角点特征进行由粗到精的匹配,完成影像的配准;并利用MODIS影像、TM影像和HJ-1卫星影像3种不同分辨率的遥感影像进行配准实验,结果表明该自动配准方法能够达到亚像素级的配准精度,是一种高精度的影像配准方法。     

基于Skyline的数字矿山三维综合监测系统的应用研究

介绍了如何借助Skyline Web三维GIS软件作为开发平台,进行了三维场景的搭建和发布,并以实际案例阐述了数字矿山三维综合监测系统的实际应用。     

One billion points in the cloud – an octree for efficient processing of 3D laser scans

Automated 3-dimensional modeling pipelines include 3D scanning, registration, data abstraction, and visualization. All steps in such a pipeline require the processing of a massive amount of 3D data, due to the ability of current 3D scanners to sample environments with a high density. The increasing sampling rates make it easy to acquire Billions of spatial data points. This paper presents algorithms and data structures for handling these data. We propose an efficient octree to store and compress 3D data without loss of precision. We demonstrate its usage for an exchange file format, fast point cloud visualization, sped-up 3D scan matching, and shape detection algorithms. We evaluate our approach using typical terrestrial laser scans.     

国产卫星影像配准技术研究

我国西部高海拔山区的地形地貌使得在卫星影像上确定同名特征点比较困难,为了解决国产卫星影像存在的谱段偏差,本文选择了基于SIFT算法、基于区域灰度和人工配准3种方法,对ZY1-02C、ZY-3和GF-1等国产卫星影像展开了配准试验研究,应用目视法和中误差法进行了精度评价,并对结果进行了对比分析。试验结果表明：人工法虽能获得较高的配准精度,但效率很差;基于区域灰度法受影像亮度差异影响很大,同名点提取少,精度最差;基于SIFT算法自动化程度最高,可以提取大量特征点,并能筛选提出误匹配点,配准精度较高。     

利用线特征进行高分辨率影像与LiDAR点云的配准

试图从离散点云数据中寻找影像的同名点是非常困难的,因此传统的基于同名特征点的配准方法难以使用。应用共线方程作为严格配准模型,利用LiDAR点云空间中的线特征替代传统配准模型中的点特征,取得了高精度的配准结果,同时对点云密度和影像分辨率之间的尺度关系进行了半定量分析。     

集成RTK的三维激光扫描技术测量地形的方法

针对标靶扫描、全站仪辅助等因素造成扫描作业过程的复杂繁琐,提出了集成RTK的三维激光扫描技术测量地形的整体方案。采用网络RTK同轴同步测量扫描站坐标;两级拼接策略：地物点粗拼接与基于面搜索的ICP精确配准;采用测块四角或周边RTK点进行点云绝对定向;采用自主研发的点云测图平台进行地形测绘。通过几种典型地形的实验验证,该方案使得扫描作业效率提高了约5倍,与现行全野外数字测图方法比较,作业效率提高了约3倍。