Cycle graph analysis for 3D roof structure modelling: Concepts and performance

The paper presents a cycle graph analysis approach to the automatic reconstruction of 3D roof models from airborne laser scanner data. The nature of convergences of topological relations of plane adjacencies, allowing for the reconstruction of roof corner geometries with preserved topology, can be derived from cycles in roof topology graphs. The topology between roof adjacencies is defined in terms of ridge-lines and step-edges. In the proposed method, the input point cloud is first segmented and roof topology is derived while extracting roof planes from identified non-terrain segments. Orientation and placement regularities are applied on weakly defined edges using a piecewise regularization approach prior to the reconstruction, which assists in preserving symmetries in building geometry. Roof corners are geometrically modelled using the shortest closed cycles and the outermost cycle derived from roof topology graph in which external target graphs are no longer required. Based on test results, we show that the proposed approach can handle complexities with nearly 90% of the detected roof faces reconstructed correctly. The approach allows complex height jumps and various types of building roofs to be firmly reconstructed without prior knowledge of primitive building types.     

基于图论的遥感影像误匹配点自动探测方法

提出一种基于图论的卫星遥感影像误匹配点自动剔除方法。首先以稀疏匹配点为节点分别构建左右影像的完全图;然后利用每个节点所对应三角形的相似度之和为属性值构建导出图;最后通过搜索导出图中属性值最小的节点定位误匹配点。在剔除单个误匹配点的基础上,采用重新构建完全图-导出图-定位误匹配点的循环搜索策略,达到自动探测并剔除多个误匹配点的目的。实验表明,该方法无需建立匹配点间的映射模型,仅利用三角形的相似关系就可以定位误匹配点,与广泛使用的随机抽样一致性粗差探测方法相比,对误匹配点具有更高的识别率和更低的误判率。     

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。     

Reconstructing facade semantic models using hierarchical topological graphs

Semantic information in 3D building models is of vital importance for various applications in terms of smart cities. To infer the semantic information and localize the components on building facades, this article proposes a novel approach to model facades with semantics by constructing hierarchical topological graphs. This method utilizes the topological characteristics of building facades. In the first‐layer layout graph, the algorithm takes the nearest cluster as the vertex and the distance between components as the edge. Thus, a topology graph is generated for the facade. The proposed algorithm is divided into three steps. First, the topology graph is obtained by calculating the spacing between the components. It is reasonable to calculate the topological graph by encoding the topological edges. If this calculation is not effective, the topology is justified by adjusting the spacing between components. Finally, the vertices in the graph are used to repair the occluded parts of the facade. In the second‐layer graph, a grid is constructed according to the first‐layer graph. Then, the attributes of the nodes are used to reconstruct the facade. The experimental results show that this method has a high accuracy of 90% and that the average time consumption is 6 s.     

一种高效率井下导线测量辅助系统

针对在煤矿井下导线和陀螺定向测量中,采用点下仪器对中操作过程往往耗时长且易造成明显的对中误差,并严重影响了矿井测量工作的效率和精度的问题,该文提出一种无需仪器精确对中的改进方法。通过安置一套测定对中偏差的触屏读数装置及内置数据处理系统,对观测数据进行自动偏心改正,较好地消减了井下导线测量或陀螺定向的对中误差影响。     

利用Snake移位模型构建中心型地图

中心型地图可表示空间各点到中心点的时间距离,能直观反映地理分布因交通系统影响而产生的相近性变化。其构建核心是依据控制点的位置移动确定非控制点的新位置,因此,将中心型地图的构建过程建模为地图目标的移位问题,以相对邻近图表示点之间的邻近关系,并基于控制点的位置移动计算点的初始受力,应用Snake移位方法迭代计算各点的新位置生成中心型地图,同时利用后处理解决潜在拓扑错误。利用所提方法和最小二乘方法可视化武汉市到其他各市的旅行时间,并定量、定性地进行对比分析。结果表明,所提方法能更好地避免拓扑错误,且局部形态保持更好。     

三频电离层延迟改正中多路径误差和观测噪声的削弱算法

多频测距系统可以借助多频观测数据削弱电离层延迟的影响,但多频改正算法在改正电离层延迟项的同时会不同程度地放大多路径误差、观测噪声等伪距误差的影响。其中利用三频数据可以将电离层延迟改正至二阶项,也可以只改正至一阶项,分别称为三频二阶改正和三频一阶改正。首次推导了利用三频观测数据削弱伪距中多路径效应和观测噪声等误差的算法,使三频电离层延迟改正中伪距误差的影响大大减小。通过对三频实测数据的处理和分析验证了算法的有效性并给出了一些有益的结论和建议:在利用三频观测数据进行电离层改正时,首先改正伪距中的多路径误差和观测噪声,然后采用三频二阶改正算法将电离层延迟改正至二阶项,将有效提高伪距改正精度。如果不能有效削弱这些误差的影响,宜采用三频一阶改正或双频改正。     

关键点检测的复杂建筑物模型自动重建

利用机载LiDAR点云数据进行建筑物重建是当今摄影测量与遥感领域的一个热点问题,特别是复杂形状建筑物模型的精确自动构建一直是一个难题。本文提出一种基于关键点检测的复杂建筑物模型自动重建方法,采用RANSAC法与距离法相结合的分割方法自动提取建筑物屋顶各个平面的点云,并利用Alpha Shape算法提取出各个平面的精确轮廓,根据屋顶平面之间的空间拓扑关系分析建筑物的公共交线特征,在此特征约束下对提取的初始关键点进行修正,最终重建出精确的建筑物3维模型。选取不同类型复杂建筑物与包含复杂建筑物的城市区域点云进行实验,结果表明该算法具有较强实用价值。     

Systematic Geometric Image Measurement Errors of Circular Object Targets: Mathematical Formulation and Correction

The use of circular object targets is very common in spatial photogrammetric object reconstruction. An object circle is projected on to the image plane as an ellipse if the object plane and the image plane are not parallel to each other. The image co-ordinates of the centre of the ellipse are usually determined automatically by means of digital image processing. These co-ordinates are then used as observations for a subsequent reconstruction of the three dimensional object point. The image co-ordinates of the centre of the ellipse and the true co-ordinates of the projected centre of the circular object target are not identical; thus eccentricity is caused, resulting in systematic geometric image measurement errors. This paper describes the functional context of this eccentricity for a typical target/camera set-up. The possible solutions for a correction of this systematic eccentricity error are derived. Guidelines for the correct combination of object target sizes and exposure distances, guaranteeing reliable image point measurements and accurate object point determination, are proposed.     

Morse理论支持下的建筑物点云特征提取

针对传统特征提取算法的结果存在交叉紊乱、不连续、缺少拓扑关系等问题,本文提出了基于Morse理论的建筑物点云特征提取算法。首先定义三维表面模型上顶点的Morse函数指标;然后采用邻点比较法自动提取特征点;最后针对Morse-Smale复形的对偶性在建筑物拓扑特征中已无实际意义的问题,提出了单复形拓扑模型的提取与简化算法。试验结果表明,该算法能够获得清晰、连续、完整的建筑物特征线,实现对建筑物模型表面的完全分割;简化算法在保证建筑物特征线拓扑一致性的前提下,可以获取不同层次的建筑物拓扑特征,为建筑物模型的重建与可视化提供了保障。     

基于线性矩阵不等式的智能飞行器航迹规划方法

智能飞行器在军用和民用领域发挥着越来越重要的作用. 在飞行过程中经常会出现累积的定位误差并且飞行到达应用场景时有定位精度要求,故需要对飞行轨迹进行适当的位置校正. 为此,提出了一种在复杂条件下的智能飞行器航迹规划方法,利用基于线性矩阵不等式(LMI)的优化方法实现最少校正次数和最短飞行距离的双重目标. 根据可用校正点数量以及它们对飞行器位置的不同影响,首先生成一个0~1三角变量矩阵来表示从点A开始的飞行航迹,以面向目标的方式不重复的遍历一系列校正点,并最终到达目标点;然后对航迹相关矩阵的变量项施加强制性的约束条件,将所有的约束作为一个整体转换和施加到之前定义的变量矩阵中,最后利用基于LMI的优化方法实现双重优化. 通过仿真结果验证了所提出的航迹规划方法在计算资源和优化结果方面比线性规划等其他优化方法更优越.      

数字地形建模的真三维TIN算法研究(英文)

The problem of taking an unorganized point cloud in 3D space and fitting a polyhedral surface to those points is both important and difficult. Aiming at increasing applications of full three dimensional digital terrain surface modeling, a new algorithm for the automatic generation of three dimensional triangulated irregular network from a point cloud is proposed. Based on the local topological consistency test, a combined algorithm of constrained 3D Delaunay triangulation and region-growing is extended to ensure topologically correct reconstruction. This paper also introduced an efficient neighboring triangle location method by making full use of the surface normal information. Experimental results prove that this algorithm can efficiently obtain the most reasonable reconstructed mesh surface with arbitrary topology, wherein the automatically reconstructed surface has only small topological difference from the true surface. This algorithm has potential applications to virtual environments, computer vision, and so on.     

Multiple-entity based classification of airborne laser scanning data in urban areas

There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method.Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified.Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers.Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information.     

利用层间连接和平滑策略重建屋顶三维模型

面向数字城市和智慧城市建设急需城市建筑三维模型支撑的需要,本文基于机载LiDAR数据,以“顾及平整性的屋顶面片分割—屋顶层间连接—三维模型重建”为脉络,提出了一种采用层间连接和平滑策略的建筑屋顶三维模型重建方法。在屋顶面片提取过程中,充分顾及了屋顶面片的平整性;并在屋顶面片平整基础上,提出层间连接点的概念,以实现高效、快速的模型重建工作。试验部分,本文从屋顶面片重建完整率与正确率、重建几何精度及建筑物高程对于重建的影响3个方面作了较为详尽的评价与分析,并在国际摄影测量与遥感学会标准数据集支撑下,与国际同行进行试验对比。试验结果表明,建筑屋顶重建的完整率和正确率分别达到90%和95%;在偏移距离评价方面,平均偏移距离和标准差最优分别达0.05 m和0.18 m。因此,本文方法可有效完成建筑屋顶三维模型重建,重建模型准确度高、完整性好。     

基于图割算法的摄影测量点云面向对象分类方法

针对摄影测量点云分类问题,提出一种基于图割算法的面向对象分类方法。由于摄影测量点云质量相对较差,文中首先基于区域增长分割思想,提出一种新的点云体素生成方法,将摄影测量点云聚为不同的对象;然后将不同的对象作为节点,将分类问题建模为一个多标记问题,引入图割算法优化获取邻域内平滑一致的分类结果。利用两组摄影测量点云数据进行实验,正确率分别为87.3%和88.7%,比基于单点的分类方法分别提高2%和2.6%。     

机载LiDAR点云的多层建筑物三维轮廓模型自动重建

以机载LiDAR点云数据为研究对象,提出一种新的基于点云数据的多层建筑物三维轮廓模型高精度自动重建方法。在已完成建筑物结构提取及轮廓规则化处理的基础上,利用多层屋顶轮廓在水平投影面内的相邻关系,将各层屋顶中同等级屋顶的相邻关系概括为平行边、不平行且不相交、相交3种相邻形式,结合多层屋顶的层级结构信息对相邻轮廓边界进行一致性处理。实验证明本文方法可以进一步消除多层建筑物各屋顶轮廓的规则化处理误差,使相邻轮廓边界在水平投影面内严格重合,同时重建后建筑物三维轮廓模型的正确性与完整性较高,拐点的定位精度优于激光点平均间距。     

基于2D-3D语义传递的室内三维点云模型语义分割

针对现有三维点云模型重建对象化和结构化信息缺失的问题,提出一种基于图模型的二维图像语义到三维点云语义传递的算法。该算法利用扩展全卷积神经网络提取2D图像的室内空间布局和对象语义,基于以2D图像超像素和3D点云为结点构建融合图像间一致性和图像内一致性的图模型,实现2D语义到3D语义的传递。基于点云分类实验的结果表明,该方法能够得到精度较高的室内三维点云语义分类结果,点云分类的精度可达到73.875 2%,且分类效果较好。     

利用VLL-LSM自动检查DEM数据

针对用数字摄影测量手段从遥感影像中提取的DEM,探讨了一种利用垂线轨迹法(VLL)结合最小二乘匹配(LSM)进行DEM高程数据的自动检查与编辑的方法。实验证明,在一定条件下利用该检查方法能够快速、准确地发现并纠正DEM高程数据中存在的问题。     

共面约束的机载LiDAR IMU安置角误差自动检校方法

针对机载LiDAR数据处理中IMU安置角误差检校问题,该文提出一种基于共面约束的自动检校方法。从激光点云中自动提取尖顶房屋顶平面,并建立连接平面关系,基于激光脚点坐标计算公式和共面约束条件,通过平差解算得到IMU安置角误差参数。以Riegl Q780获取的数据进行实验,该方法检校结果与RiProcess软件提供的检校参数非常接近。经过误差改正后,相邻航线获取的点云可以很好地重合在一起。相对于人工选择特征地物进行IMU安置角误差检校,该方法可以大大提高检校的效率和可靠性。     

利用多边形骨架线生成房屋模型算法研究

城市作为人类生活的重要聚集地,是三维GIS应用的主要领域。本文结合房屋的二维轮廓数据与屋顶的基本信息（倾斜角度、屋顶高度、纹理属性）,以“骨架线提取-房屋模型构建”为框架,提出了一种基于多边形骨架线自动生成简单房屋模型的算法,详细描述了该方法的基本思想,并在试验结果的基础上证实了该算法的有效性。