TLS技术支持下的边坡点云重构与形变分析

针对边坡地形点云存在空洞、分布不均匀,以及直接建立表面模型进行形变分析效果差的问题,首先提出了一种改进反距离权重插值对地形点云快速压缩重构的方法,并以程序实现了该方法的自动化处理;然后快速地对压缩重构点云建立了NURBS曲面模型,并从面、线、点进行了全面形变对比分析;最后将计算结果与实测值进行了一致性检验,结果表明符合实际地形变化情况,实现了利用激光点云数据快速构建边坡模型并进行形变分析的目的。     

基于二次曲线拟合的隧道激光点云滤波方法及其应用

提出了利用二次曲线拟合地铁隧道中轴线方法, 建立了地铁里程与隧道盾构环片之间的对应关系。首先沿中轴线对隧道盾构环片进行分割, 针对分割后盾构片上附着的金属支架等噪声点, 提出基于隧道设计半径的粗滤噪和基于多项式拟合的精滤噪相结合的滤噪方法, 然后对滤噪后的两期隧道点云分别建立数字表面模型(DSM)并进行叠加分析, 获取不同里程处的隧道形变情况。试验表明, 该方法可获得地铁隧道内任意处的形变量, 可为相关研究工作提供借鉴。     

一种顾及主方向的点云配准施工检测方法

面向建筑物施工的全局偏差检测需求,提出一种顾及主方向的PCA-ICP点云配准施工检测方法。首先将BIM设计模型数据转换为BIM点云数据;然后运用顾及主方向的PCA (主成分分析)粗配准结合ICP (迭代最近点)精配准,即顾及主方向的PCA-ICP配准方法,在无控制点条件下实现三维激光点云与BIM点云配准,并计算配准均方误差E;最后利用M₃C₂算法和均方误差E计算出三维激光点云与BIM点云之间的偏差值和偏差值误差范围,最终统计得到施工偏差结果。以某体育场馆钢结构为例,实验结果表明,该方法较常规偏差检测方法可以得到建筑物实际施工的全局偏差。实验结果可为建筑物施工提供参考。     

法线特征约束的激光点云精确配准

作为点云数据处理的关键步骤,配准结果直接影响到后续数据处理的精度。针对传统迭代最近点(iterative closest point,ICP)算法依赖较好初始位置的局限性,提出基于法线特征约束的点云精确配准方法。首先采用局部表面拟合方法进行法线估计,并计算其快速点特征直方图,然后通过采样一致性方法对两组点云进行粗配准,最后通过建立KD-Tree加快对应点的搜索效率,并设定阈值去除错误对应点对,实现精确配准。结果表明,基于法线特征约束的粗配准算法可以为待配准点云提供较好的初始位置,并且改进的ICP算法有效地提高了点云配准的精度和效率。     

一种基于全站扫描的特征点自约束点云变形分析方法

为了对大坝、高层建筑、滑坡区、采空区等危险变形体进行变形观测,针对智能全站仪、GNSS测量、三维激光扫描等变形监测技术无法很好地实现变形特征点和点云的综合变形分析问题,提出了基于全站扫描特征点自约束点云变形分析方法,获取变形体的离散特征点和整体点云变形数据,利用特征点形变矢量求取点云至模型的形变量,从而刻画变形体的整体形变信息。试验结果表明,利用本文所提出的方法能够成功计算出点云至模型的形变量,经统计分析,所有点的形变量真误差的期望值为-0.04 mm,结果精度为1.2 mm。试验结果能够反映变形体的整体形变信息,且具有较高精度。     

地基合成孔径雷达的边坡形变监测应用研究

针对露天矿开采过程中边坡发生形变导致失稳的问题,使用地基合成孔径雷达对矿坑边坡进行形变监测.基于小基线集技术,该文提出以PS点相位相干系数的均值作为子集划分的参考指标,并提出在子集内采用粗差探测技术对PS点进行时间维解缠,有效削弱了子集内的大气延迟影响并防止了解缠误差在空间域的传递.通过首云铁矿边坡形变监测实验,结果表明:该文提出的方法能有效处理GB-SAR边坡连续监测数据,能够获取露天矿边坡PS点在监测期的时间序列形变,该方法在边坡稳定性监测方面具有实用性.     

改进ICP算法的激光雷达点云配准

针对传统ICP算法在激光雷达目标点云配准中存在匹配时间长,以及受初值影响导致该算法应用在无人车SLAM技术中容易存在定位精度不高和稳健性较差的问题,本文提出了一种结合KD-tree算法的NDT-ICP算法。首先,通过Voxel Grid滤波对激光雷达获取的点云数据进行预处理,利用平面拟合参数的方法去除地面点云;然后,利用NDT算法进行点云粗匹配,缩短目标点云与待匹配点云距离;最后,通过KD-tree邻近搜索法提高对应点查找速度,并通过优化收敛阈值,完成ICP算法的精匹配。试验结果表明,本文提出的改进算法相比于NDT算法和ICP算法,在点云配准速度和精度上有明显提高,且在地图构建上精度和稳健性更好。     

基于特征点法向量的点云配准算法

在传统的迭代最近点算法（ICP）中,需要两片点云具有良好的初始位置,否则在配准时容易陷入局部最优。针对该问题,本文提出了一种基于特征点提取与配对的粗配准方法,以调整两片点云重叠部分的初始位置。首先,利用SIFT算法提取两片点云公共部分的特征点;其次,根据特征点法向量之间的欧氏距离将两片点云的特征点两两配对;然后,利用法向量的夹角对特征点对进行提纯;最后,通过单位四元数法,求解出旋转及平移矩阵,完成粗配准。试验表明,本文基于特征点法向量的粗配准方法可为精配准提供良好的初始位置,在一定程度上避免配准时陷入局部最优的现象。     

地面激光扫描目标的变形信息探测

介绍了基于平面拟合后假设检验方法进行形变探测的算法,提出了逐级划分点云的方法,利用试验数据实现了最佳划分方案探测,提高了形变探测效率,并在最后一级划分中提取出形变量。     

利用三维激光扫描技术检测工业构件螺栓孔的空间位置

面向工业检测领域提出了一种工业构件螺栓孔的空间位置检测方法,解决了大型工业钢结构特征位置的检测问题。通过三维激光扫描获得构件的完整点云,提取螺栓孔的点云,利用非线性的最小二乘方法拟合螺栓孔的中心,获得螺栓孔中心在点云坐标系中的坐标。同时,根据设计尺寸建立构件的标准模型作为检测的基准,将模型特征转换成一组点表示,拟合对应的螺栓孔中心,获得螺栓孔中心在模型坐标系中的坐标。利用两个坐标系中若干个公共点计算平移矩阵和旋转矩阵,最后将点云坐标系中的坐标转换到模型坐标系中,计算出标准模型与构件对应点之间的误差,从而实现对工业构件螺栓孔的空间位置精度检测。     

球形标靶的固定式扫描大点云自动定向方法

根据目前地面激光扫描数据获取速度快、数据量大、测量距离远、专用特殊材料制作的标靶识别距离近、点云定向数据处理相对滞后、自动化程度低、不能适应远距离地形测量的现状,提出了从大点云中(每站1亿点以上)自动探测远距离标靶的点云定向方法。该方法首先根据标靶控制点的工程测量坐标信息,搜索到标靶所在点云环,然后对各点云环进行扇形分区,快速探测标靶,获取标靶中心扫描坐标,最后平差计算扫描仪位置参数和姿态参数,实现点云坐标到工程测量坐标的转换。该方法在普通配置的计算机上得到实现,并成功用于远距离山区地形测量,其中定向标靶半径0.162m,标靶到扫描站距离在180~700m之间。     

TLS三维点云聚类滤波算法应用研究

现有地面三维激光扫描点云数据滤波算法较少,针对地形复杂区域的点云滤波效果更是不甚理想,因此对二维聚类算法进行改进,提出三维点云聚类滤波算法,并对其在地形复杂区域的TLS数据滤波中的应用进行研究。以重庆鸡冠岭危岩体的TLS数据为例,分别采用曲率平滑滤波方法和文中提出的点云聚类滤波方法处理,并对两种方法处理过的数据进行形变量计算和分析。实验证明,针对植被覆盖茂密、地形复杂的山体,该方法的点云滤波效果较好,且处理速度有较大提升,能为点云后期形变量计算提供较好的基础。     

利用TLS技术与伪单点监测模式进行滑坡变形测量

针对传统离散点监测模式不适用于TLS变形监测,以及现有基于TLS技术变形监测的一维性与仅适用于特定几何对象分析等不足,本文提出了一种基于TLS技术的伪单点变形监测模式,并基于该监测模式提出了一种以表面匹配技术为核心的变形计算新方法。通过构建广义高斯-马尔可夫模型匹配搜索估计伪单点对象的全三维变形参数,利用TLS多期点云的高密度性,实现了高精度提取全三维变形信息,尤其适用于滑坡等灾害监测领域。试验表明在扫描距离约240 m时,该方法可达到1 cm级的变形提取精度,对推动变形监测从点测绘向形测绘的转变具有一定的价值。     

一种由粗到精的地面激光点云多层级配准方法

提出了一种综合利用快速点特征直方图(FPFH)描述符和同名点引导ICP优化的地面激光扫描(TLS)点云配准方法。该方法包括3个步骤:1)点云金字塔构建;2)基于FPFH的粗配准;3)同名点引导的ICP精配准。首先使用体素网格滤波器构造点云的金字塔结构,在粗配准时,FPFH描述符用于金字塔顶层上点云的鲁棒匹配,在此基础上,再进行两层级同名点引导的ICP精配准优化,使用3组典型TLS点云对进行实验,结果表明本文方法可以高效地完成TLS点云的配准。     

A laser scanning-based method for fast estimation of seismic-induced building deformations

Monitoring damaged buildings in an area where an earthquake has occurred requires the use of techniques which provide rapid and safe measurements even in emergency conditions. In particular, remote sensing techniques like terrestrial laser scanning (TLS) can satisfy these requirements, since they produce very dense point clouds in little time and also allow an accurate geometric modeling of observed buildings. Nevertheless, strong constraints on TLS data acquisition geometry, such as acquisition distance and incidence angles, typically characterize an area in seismic emergency conditions. In order to correctly interpret the data, it is necessary to estimate errors affecting TLS measurements in these critical conditions. A reliable estimation can be achieved by means of experiments and numerical simulations aimed at quantifying a realistic noise level, with emphasis on reduction of artifacts due to data acquisition, registration and modeling. This paper proposes a data analysis strategy in which TLS-based morphological maps computed as point-to-primitive differences are created. The method can be easily used for accurate surveying in emergency conditions. In order to demonstrate the proposed method in very diverse situations, it was applied to rapidly detect deformation traces in the San Giacomo Roncole Campanile (Modena), the Asinelli tower (Bologna) and the Cantalovo Church (Verona), three buildings damaged by the Mw 5.9 Emilia Romagna 2012 earthquake (Italy).     

地面LiDAR数据模拟及单木LAI反演

地面激光雷达Li DAR可以快速获取高精度、高密度的点云数据,在植被结构参数获取方面的应用越来越广泛。为了定量分析地面激光雷达点云数据获取单木结构参数的能力和精度,本文提出利用光线跟踪结合植被真实结构模拟地面3维激光扫描仪的单木点云数据(以RIEGL VZ-1000为例),并结合间隙率模型反演单木叶面积指数LAI。在点云模拟过程中,充分考虑了脉冲特性,包括光斑大小、波束发射角以及回波探测强度。重点分析了光斑大小和最小探测强度对LAI反演的影响,并采用根河实测单木数据进行了验证。结果表明,光斑大小和最小探测强度的设定对于LAI反演结果存在很大影响,该结论对于提高地面激光雷达点云数据反演植被结构参数精度具有重要意义。     

Feature Extraction of Laser Scan Data Based on Geometric Properties

Terrestrial Laser Scanner (TLS) is an important device to provide three-dimensional geometric information with respect to known reference coordinates system. The data from TLS are often called point clouds due to their high density. They are unstructured and do not have any explicit information about the object, except 3D positional and intensity information to each point. Data segmentation and feature extraction are fundamental problems in the point cloud processing step. These are important prerequisites for object recognition and environment understanding. This paper describes a methodology to use the geometrical information of the scanned object, such as a normal vector to extract and segment object edges. The edge based segmentation technique will be studied and improved. The proposed method is programmed and used to extract different region boundaries for two real TLS data, and the quality of the results shows the effectiveness of the proposed algorithm.     

Automatic Tree Identification and Diameter Estimation Using Single Scan Terrestrial Laser Scanner Data in Central Indian Forests

Forest inventory parameters, primarily tree diameter and height, are required for several management and planning activities. Currently, Terrestrial Laser Scanning (TLS) is a promising technology in automated measurements of tree parameters using dense 3D point clouds. In comparison with conventional manual field inventory methods, TLS systems would supplement field data with detailed and relatively higher degree of accurate measurements and increased measurement frequency. Although, multiple scans from TLS captures more area, they are resource and time consuming to ensure proper co-registration between the scans. On the other hand, Single scans provide a fast and recording of the data but are often affected by occlusions between the trees. The current study evaluates potential of single scan TLS data to (1) develop an automatic method for tree stem identification and diameter estimation (diameter at breast height—DBH) using random sample consensus (RANSAC) based circle fitting algorithm, (2) validate using field based measurements to derive accuracy estimates and (3) assess the influence of distance to scanner on detection and measurement accuracies. Tree detection and diameter measurements were validated for 5 circular plots of 20 m radius using single scans in dry deciduous forests of Betul, Madhya Pradesh. An overall tree detection accuracy of 85 and 70% was observed in the scanner range of 15 and 20 m respectively. The tree detection accuracies decreased with increased distance to the scanner due to the decrease in visible area. Also, estimated stem diameter using TLS was found to be in agreement with the field measured diameter (R² = 0.97). The RMSE of estimated DBH was found to be 3.5 cm (relative RMSE ~20%) over 202 trees detected over 5 plots. Results suggest that single scan approach suffices the cause of accuracy, reducing uncertainty and adds to increased sampling frequency in forest inventory and also implies that TLS has a seemingly high potential in forest management.     

The effect of short ground vegetation on terrestrial laser scans at a local scale

Terrestrial laser scanning (TLS) can record a large amount of accurate topographical information with a high spatial accuracy over a relatively short period of time. These features suggest it is a useful tool for topographical survey and surface deformation detection. However, the use of TLS to survey a terrain surface is still challenging in the presence of dense ground vegetation. The bare ground surface may not be illuminated due to signal occlusion caused by vegetation. This paper investigates vegetation-induced elevation error in TLS surveys at a local scale and its spatial pattern. An open, relatively flat area vegetated with dense grass was surveyed repeatedly under several scan conditions. A total station was used to establish an accurate representation of the bare ground surface. Local-highest-point and local-lowest-point filters were applied to the point clouds acquired for deriving vegetation height and vegetation-induced elevation error, respectively. The effects of various factors (for example, vegetation height, edge effects, incidence angle, scan resolution and location) on the error caused by vegetation are discussed. The results are of use in the planning and interpretation of TLS surveys of vegetated areas.