Accuracy of tree mapping based on hand-held laser scanning comparing leaf-on and leaf-off conditions in mixed forests

The use of mobile laser scanning to survey forest ecosystems is a promising,scalable technology to describe forest 3D structures at high resolution.To confirm the con-sistency in the retrieval of forest structural parameters using hand-held laser scanning(HLS),before operationalizing the method,confirming the data is crucial.We analyzed the per-formance of tree-level mapping based on HLS under differ-ent phenology conditions on a mixed forest in western Spain comprising Pinus pinaster and two deciduous species,Alnus glutinosa and Quercus pyrenaica.The area was surveyed twice during the growing season(July 2022)and once in the deciduous season(February 2022)using several scan-ning paths.Ground reference data(418 trees,15 snags)was used to calibrate the HLS data and to assess the influence of phenology when converting 3D data into tree-level attrib-utes(DBH,height and volume).The HLS-based workflow was robust at isolating tree positions and recognizing stems despite changes in phenology.Ninety-six percent of all pairs matched below 65 cm.For DBH,phenology barely altered estimates.We observed a strong agreement when comparing HLS-based tree height distributions.The values exceeded 2 m when comparing height measurements,confirming height data should be carefully used as reference in remote sensing-based inventories,especially for deciduous species.Tree volume was more precise for pines(r=0.95,and rela-tive RMSE=21.3–23.8%)compared to deciduous species(r=0.91–0.96,and relative RMSE=27.3–30.5%).HLS data and the forest structural complexity tool performed remark-ably,especially in tree positioning considering mixed forests and mixed phenology conditions.

Automated extraction of expressway road surface from mobile laser scanning data

This paper presents a voxel-based region growing method for automatic road surface extraction from mobile laser scanning point clouds in an expressway environment.The proposed method has three major steps:constructing a voxel model;extracting the road surface points by employing the voxel-based segmentation algorithm;refining the road boundary using the curb-based segmentation algorithm.To evaluate the accuracy of the proposed method,the two-point cloud datasets of two typical test sites in an expressway environment consisting of flat and bumpy surfaces with a high slope were used.The proposed algorithm extracted the road surface successfully with high accuracy.There was an average recall of 99.5%,the precision was 96.3%,and the F1 score was 97.9%.From the extracted road surface,a framework for the estimation of road roughness was proposed.Good agreement was achieved when comparing the results of the road roughness map with the visual image,indicating the feasibility and effectiveness of the proposed framework.

Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters

Sustainable forest management heavily relies on the accurate estimation of tree parameters.Among others,the diameter at breast height(DBH) is important for extracting the volume and mass of an individual tree.For systematically estimating the volume of entire plots,airborne laser scanning(ALS) data are used.The estimation model is frequently calibrated using manual DBH measurements or static terrestrial laser scans(STLS) of sample plots.Although reliable,this method is time-consuming,which greatly hampers its use.Here,a handheld mobile terrestrial laser scanning(HMTLS) was demonstrated to be a useful alternative technique to precisely and efficiently calculate DBH.Different data acquisition techniques were applied at a sample plot,then the resulting parameters were comparatively analysed.The calculated DBH values were comparable to the manual measurements for HMTLS,STLS,and ALS data sets.Given the comparability of the extracted parameters,with a reduced point density of HTMLS compared to STLS data,and the reasonable increase of performance,with a reduction of acquisition time with a factor of5 compared to conventional STLS techniques and a factor of3 compared to manual measurements,HMTLS is considered a useful alternative technique.

A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner

Roof bolts such as rock bolts and cable bolts provide structural support in underground mines.Frequent assessment of these support structures is critical to maintain roof stability and minimise safety risks in underground environments.This study proposes a robust workflow to classify roof bolts in 3 D point cloud data and to generate maps of roof bolt density and spacing.The workflow was evaluated for identifying roof bolts in an underground coal mine with suboptimal lighting and global navigation satellite system(GNSS)signals not available.The approach is based on supervised classification using the multi-scale Canupo classifier coupled with a random sample consensus(RANSAC)shape detection algorithm to provide robust roof bolt identification.The issue of sparseness in point cloud data has been addressed through upsampling by using a moving least squares method.The accuracy of roof bolt identification was measured by correct identification of roof bolts(true positives),unidentified roof bolts(false negatives),and falsely identified roof bolts(false positives)using correctness,completeness,and quality metrics.The proposed workflow achieved correct identification of 89.27%of the roof bolts present in the test area.However,considering the false positives and false negatives,the overall quality metric was reduced to 78.54%.

Scan matching using line segment relationships for map-based localization of mobile robot

A new scan matching method for mobile robot localization is presented, which takes line segment as the feature and matches the real scans in the given reference map by relationships of the directional-defined line segments. The alignment was done by hierarchically identifying the multiple relationships and the result was recorded in a correspondence matrix, where the best match is defined and selected for localization. It is indicated that the searching algorithm of the best match can find the ambiguities and get rid of them. This method with less computational cost works well in occluded environment, and can correct the error in pose estimation without the need for the estimation itself. The efficiency, accuracy and robustness of this method were verified by experiments of localization in an occluded environment and a long-distance indoor navigation.

A Simple Deep Learning Network for Classification of 3D Mobile LiDAR Point Clouds

Automatic and accurate classification is a fundamental problem to the analysis and modeling of LiDAR(Light Detection and Ranging)data.Recently,convolutional neural network(ConvNet or CNN)has achieved remarkable performance in image recognition and computer vision.While significant efforts have also been made to develop various deep networks for satellite image scene classification,it still needs to further investigate suitable deep learning network frameworks for 3D dense mobile laser scanning(MLS)data.In this paper,we present a simple deep CNN for multiple object classification based on multi-scale context representation.For the pointwise classification,we first extracted the neighboring points within spatial context and transformed them into a three-channel image for each point.Then,the classification task can be treated as the image recognition using CNN.The proposed CNN architecture adopted common convolution,maximum pooling and rectified linear unit(ReLU)layers,which combined multiple deeper network layers.After being trained and tested on approximately seven million labeled MLS points,the deep CNN model can classify accurately into nine classes.Comparing with the widely used ResNet algorithm,this model performs better precision and recall rates,and less processing time,which indicated the significant potential of deep-learning-based methods in MLS data classification.

基于移动多线激光雷达扫描的树冠叶面积估计方法

移动单线激光雷达(Laser detection and ranging,LiDAR)扫描(Mobile single-layer LiDAR scanning,MSLS)树冠叶面积估计方法使用单一视角的单线激光雷达采集树冠点云数据,获取的冠层信息不够全面,限制了树冠叶面积估计精度。本文提出一种基于移动多线LiDAR扫描(Mobile multi-layer LiDAR scanning,MMLS)的树冠叶面积估计方法,使用多线LiDAR从多个视角采集树冠点云数据,提升树冠叶面积估计精度。首先,将多线LiDAR采集的点云数据变换到世界坐标系下,通过感兴趣区域(Region of interest,ROI)提取出树冠点云。然后,提出一种MMLS树冠点云融合方法,逐个融合单个激光器采集的树冠点云,设置距离阈值删除重复点,添加新点。最后,构建MMLS空间分辨率网格,建立基于树冠网格面积的树冠叶面积估计模型。实验使用VLP-16型多线LiDAR传感器搭建MMLS系统,设置1、1.5 m 2个测量距离和间隔45°的8个测量角度对6个具有不同冠层密度的树冠进行数据采集,共得到96个树冠样本。采用本文方法,树冠叶面积线性估计模型的均方根误差(Root mean squared error,RMSE)为0.1041 m^(2),比MSLS模型降低0.0578 m^(2),决定系数R^(2)为0.9526,比MSLS模型提高0.0675。实验结果表明,本文方法通过多线LiDAR多视角树冠点云数据采集、MMLS树冠点云融合和空间分辨率网格构建,有效提升了树冠叶面积估计精度。

移动式三维激光扫描在轨道交通结构变形监测中的应用

针对轨道交通作业窗口“受限”且传统测量手段无法全面获取隧道状态的问题,本文借助移动式三维激光扫描系统,结合实际工程案例对轨道交通结构进行检测,首先推导了三维激光扫描的基本原理和坐标转换数学模型,通过外业数据采集、内业数据处理获取了隧道点云数据。对比分析临近地铁隧道的深基坑项目施工前后三维激光扫描数据,结果表明:基坑施工过程对隧道结构产生了一定影响,且基坑正对影响区域结构变形最大,外扩影响较小,净空收敛累计变化量最大,为15.4mm。同时使用全站仪对三维激光扫描获取的净空收敛结果进行精度评估,结果证实了:移动式三维激光扫描测量隧道净空收敛和全站仪测量净空收敛累计变化量总体一致性较好,其中83.33%的监测断面累计变化量互差小于2mm。

基于移动三维激光扫描技术的地铁隧道接触网导高检测新方法

[目的]地铁隧道内接触网导高是保证列车正常受电的重要参数,需定期进行检测。针对其他导高检测方式存在的误差大、精度低的问题,需提出基于移动三维激光扫描技术的地铁隧道接触网导高检测新方法。[方法]基于移动三维激光扫描技术提出了导高检测的新方法,分析了误差来源;提出通过在移动式三维激光扫描仪上增设刚性倾斜组件来改变扫描仪激光线入射角度,进而减少隧道断面点云模型的毛刺数量,并采用等角兰勃特投影来获取隧道真实点云模型。与导高仪检测结果进行对比,以验证其导高检测精度是否满足相关要求。[结果及结论]基于移动式三维激光扫描技术建立的隧道断面点云模型存在毛刺,是因激光脚点垂直入射接触网发生散射和漫反射导致的。增加倾斜组件,可有效减少隧道断面点云模型的毛刺数量。移动式三维激光扫描仪与导高仪的导高检测数据对比结果表明,基于移动式三维激光扫描技术的导高检测精度基本在±3 mm以内,能够满足相关规范对导高检测的精度要求。

跨模态特征融合模板驱动的盾构隧道管片孔缝智能识别

基于点云的隧道数据处理方法已逐步替代人工用于盾构管片接缝识别中。但常规点云处理算法提取螺栓孔和接缝时会存在因附属设施遮挡造成错误识别的问题,导致目标识别精度降低。针对此问题,以广州2003年开通的某地铁线路某段地铁隧道数据为例,提出一种跨模态特征融合模板驱动的盾构隧道孔缝识别方法。首先将隧道断面点云的几何中心作为视点,以扫描测线为单元进行逐测线投影生成隧道二维图像;然后通过Canny边缘检测和Hough变换缓冲识别纵缝,对二维图像进行环片分割,并基于双模板匹配实现盾构环分类,依据环片模板进行螺栓孔和横缝的粗定位;最后利用螺栓孔点云DBSCAN聚类后的中心坐标对环片模板进行精校正,实现盾构隧道管片孔缝智能识别。研究结果表明:该方法在附属设施遮挡干扰等情况下能较好地实现孔缝精确识别。其中,设计的基于局部形态特征双模版驱动的盾构环片分类方法,可实现盾构环片的精确分类;设计的顾及盾构管片空间位置关系的孔缝识别方法,可有效提升孔缝识别精度。在识别率与耗时相近的情况下,本方法比同类方法识别精度更高,平均偏差更少,具有更好的准确性和鲁棒性。该方法将盾构隧道三维点云和二维点云投影图像进行数据特征融合的同时,能够顾及局部形态特征凭借双模板实现盾构环片的精确分类以及依据盾构管片的空间位置关系进一步提高孔缝识别精度。研究结果为进一步自动化精准识别盾构隧道接缝和螺栓孔目标信息提供参考。

移动激光扫描的轨道交通站台限界测量方法与精度评定

为保证城市轨道交通运行安全,在轨道交通轨行区内安装设施设备需要满足限界要求,需要进行现场限界测量。移动激光扫描方法是一种非接触式测量技术,本文使用自主集成的隧道移动激光扫描系统,可以完整获取轨道交通车站的三维空间信息,基于二叉堆的最小优先队列和KD树等数据结构,建立点云的空间索引,对点云快速检索寻找特征点,计算站台限界值。经过项目实践与全站仪测量比对,本文方法与全站仪测量偏差均值为-0.6 cm,测量精度达0.84 cm。本文方法具有检测效率高、自动化程度高等特点,对各类交通市政工程限界测量问题具有参考意义。

移动式三维激光扫描系统在崖墓考古绘图中的应用

崖墓是四川地区具有地域特色的丧葬形式,数量众多,分布范围广泛。在分析墓葬间的空间分布关系方面,传统测量方法存在作业效率低、总体精度差等问题。因此,以新川创新科技园屈家山2号文物点为例,首先采用基于即时定位与地图构建技术的移动式三维激光扫描系统获取点云数据,再利用专业软件准确绘制墓葬分布图,并与测距仪测量结果进行对比分析。结果表明,移动式三维激光扫描系统可明显提高绘制崖墓分布图的作业效率,且满足测量精度要求,在考古领域中具有较好的应用前景。

移动扫描技术在隧道管片变形监测中的应用

地铁隧道管片收敛及错台的变形是地铁盾构隧道结构变形两种常见形态,是判定盾构隧道结构安全的两个重要指标。针对传统测量方法在对运营地铁隧道管片收敛与错台测量时的局限性引入了移动三维激光扫描技术,研究使用移动激光扫描技术来获取点云断面数据,对所采集的点云数据进行管片圆环拟合,并计算出直径收敛值及相邻环的最大错台量。通过实际工程案例对比移动三维激光扫描点云拟合获得的直径收敛、相邻环错台变形量与测距仪、全站仪等传统测量手段获得的测量值,得出移动三维激光扫描测量技术具有高效率、低人工、高精度等优点。

移动三维激光扫描技术在城市测量中的应用研究

随着测绘项目对成果要求越来越精细,移动测量技术逐渐兴起。移动三维激光扫描技术根据载具的不同可划分为背包式三维激光扫描仪、手持式三维激光扫描仪、机载三维激光扫描仪、车载三维激光扫描仪及推车式三维激光扫描仪。采用的点云绘图软件包括AutoCAD,LidarFeature及EPS。本文针对不同载具的移动测量技术及相关的处理软件的的优缺点,对不同移动测量技术的适用性进行了分析。

基于移动作业的电力现场异常智能化稽查方法研究

针对电力现场人工稽查危险性高、准确率低的问题,提出基于移动作业的电力现场异常智能化稽查方法。采集电力电场周围的电流变化数据,根据安倍定律得出电缆内外部形成的环形闭合电流。建立空间三点坐标系,计算电缆在三个方向上闭合电流值,若电流值超出既定阈值,判定周围存在磁场影响。模拟矩形的稽查空间体,在该空间体中计算待稽查点间位置关系,将位置阈值代入到异常判定矩阵中,输出异常信号位置。实验结果表明,所提方法可充分利用电力现场的暂态数据,稽查结果符合现场实际情况,具有适用性强、稽查覆盖范围广、准确率高的优势。

移动式三维激光扫描技术在石窟文物数字化保护中的应用

本文指出石窟文物是人类文明发展的珍贵遗产,具有极高的历史价值、文化价值和艺术价值。由于各种自然因素和人为因素,石窟文物常受到一定程度破坏,因此亟需进行数字化保护,但是由于石窟文物一般嵌于岩体内部,传统地面式三维激光扫描技术和倾斜摄影测量技术进行窟内数据采集较为困难。针对这一难题,本文应用移动式三维激光扫描技术采集石窟文物点云数据,通过三维建模真实还原了石窟内外部真实场景,并进行了数字化保护应用研究。研究结果表明,该技术可为石窟文物的修复保护工程提供数据基础。

基于移动三维激光扫描技术的高层建筑火灾调查研究

为了提升高层建筑火灾调查的准确度和效率,提出基于移动三维激光扫描技术的高层建筑火灾事故调查方法。在灾后现场调查中,此方法仅通过人工手持扫描仪获取事故现场的数据,结合自动化软件对收集的现场数据进行处理,实现灾后现场点云数据的全面采集,通过实际工程进行对比验证。结果显示,该方法不仅可以实现灾后现场的快速扫描和高效测量,获取的灾后现场数据可为高层建筑的安全监控提供数据支撑。

融合激光点云的低空倾斜摄影测量技术在旧城改造中的应用

老旧小区的改造提升是切实关系到民生福祉的重要工程,在旧城改造中高效、精准地采集目标建筑物空间数据对工程造价核算、规划设计等都具有重要意义。常见的三维激光扫描及倾斜摄影测量技术受扫描视角及影像畸变等影响均存在一定局限性。本文将移动式三维激光扫描仪获取的点云数据和低空倾斜摄影测量生成的点云数据进行融合,研究结果表明,本文提出的综合测量法绘制的立面成果图精度提升明显,且效率与传统全站仪法相比具有较高的优越性,对提升旧城改造工程质量具有一定参考价值。

基于三维激光扫描技术的建筑物三维模型构建方法研究

三维激光扫描技术已成为建筑物三维模型构建的重要测量手段,其三维激光点云数据获取主要通过架站式或手持移动式三维激光扫描仪。本文简要介绍了两种三维激光扫描仪的工作原理及三维激光点云的数据处理流程,并以项目工程实例详细阐述了基于三维激光扫描技术的建筑物三维模型构建方法,并以解析法实测部分建筑物角点进行精度验证。结果表明该方法能满足室内空间大且场景复杂建筑物的三维模型构建。

基于移动激光扫描的轨道交通建筑限界一体化检测

移动激光扫描可快速获取被测物体表面的三维坐标数据,近年来被逐渐应用到轨道交通工程测量项目中。针对当前轨道交通种类繁多而其限界检测研究分散,不利于工程实践的问题,本文提出了一种基于移动激光扫描技术的轨道交通建筑限界一体化检测方案。自主研发了轨道移动激光测量与检测(TLSD)系统,建立系统内部数据采集、处理、分析的限界检测流程,并集成各类现行限界规范标准,可同时满足铁路、地铁等不同轨道交通限界检测的需要。结合某铁路站台进行应用实践,结果显示,TLSD系统具有检测高效、自动化、成果可视化等优势。