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.

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.

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.

移动激光扫描在地铁车站限界测量中的应用

为满足限界要求,地铁车站站台边缘与地铁列车之间存在一定的间隙,但可能发生乘客踏空风险。现需对车站区段的站台边缘至线路中心线距离现状进行快速普查,为更新改造提供决策数据。文章利用移动激光扫描技术快速获取车站段全断面点云,通过点云数据处理提取关键特征点,实现了对空间距离的非接触测量,经传统方法对比复核,验证了该方法的准确性。

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。

基于MLS点云的多尺度盾构隧道渗水病害检测网络

在盾构隧道中,渗水问题常导致电力系统短路、设备腐蚀以及结构劣化等后果。然而,现有的渗水病害检测技术存在智能化程度低、精度和效率不足的问题。针对这一挑战,提出一种基于点云强度图像的智能化渗水病害检测方法。首先,利用移动激光扫描设备在弱光环境的隧道条件下收集三维点云数据,标注构建了渗水病害数据集。然后,针对多尺度渗水目标,设计了高性能的目标检测网络。该网络集成了专为处理多尺度渗水信息而设计的可重构上下文信息融合模块,并设计了渗水的难例挖掘损失函数,以提高模型对多尺度渗水目标和具有挑战性目标的检测能力。为简化模型复杂度,使用改进后的轻量化检测头结构减小模型的大小。最后,通过在渗水病害数据集上进行训练和测试,实验结果表明,该模型实现了93.59%的渗水病害识别率,AP指标相比原模型提高了3.69%。此外,改进后的模型相比于原模型计算量减少了47.15%。消融实验和对比实验进一步验证了该方法的有效性以及相对于其他方法的优势。总体而言,该方法在效率和精度上都取得了显著效果,为盾构隧道渗水病害检测提供了有效保障,有助于确保隧道的安全运行。

考虑MLS点云邻域特征的道路附属设施检测

为高效准确地获取道路附属设施运营现状,提出了一种考虑点云邻域特征的道路附属设施检测方法.首先,结合点云邻域特征和行车轨迹点,构建基于最近行车轨迹点的数据索引和伪坐标;其次,利用主成分分析法和网格化搜索法,设计从下部杆状物提取到上方点补全的两阶段法,实.现路侧杆状附属设施检测;然后,结合道路边界和行车轨迹高程基准进行路内上方附属设施提取,通过最小二乘法开展净空分析.结果表明,数据集中路侧杆状的检测精确率和召回率分别超过91%和90%,路内上方附属设施检测精确率和召回率分别超过93%和92%,且计算时间不超过20 s,满足工程需求.由于下部杆状物体被遮挡,部分路侧杆状附属设施的检测存在误差.采用所提方法计算得到的道路净空误差均小于0.1 m,具有较高的可行性和精确性,能够满足附属设施提取、检测和管理的需求.

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。

移动扫描技术在建筑测绘中的应用

移动扫描技术利用激光雷达传感器结合SLAM技术能够快速、准确地获取目标的点云数据,相较于传统的三维激光扫描技术,它不需要布设控制点和设置标靶,省去了点云数据预处理过程中的点云拼接步骤。文章综合分析了移动扫描技术在建筑测绘中的应用效果,结果表明,移动扫描技术除了具备传统三维激光扫描技术的优点外,在提供工作效率方面具有明显优势,为建筑测绘提供了新的技术方法。

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。

Mixed reality head mounted displays for enhanced indoor point cloud segmentation with virtual seeds

Mixed Reality(MR)Head Mounted Displays(HMDs)offer a hitherto underutilized set of advantages compared to conventional 3D scanners.These benefits,inherent to MR-HMDs albeit not originally intended for such appli-cations,encompass the freedom of hand movement,hand tracking capabilities,and real-time mesh visualization.This study leverages these attributes to enhance indoor scanning process.The primary innovation lies in the con-ceptualization of manual-positioned MR virtual seeds for the purpose of indoor point cloud segmentation via a region-growing approach.The proposed methodology is effectively implemented using the HoloLens 2 platform.An application is designed to enable the remote placement of virtual tags based on the user’s visual focus on the MR-HMD display.This non-intrusive interface is further enriched with expedited tag saving and deletion functionalities,as well as augmented tag visualization through overlaying them on real-world objects.To assess the practicality of the proposed method,a comprehensive real-world case study spanning an area of 330 s^(2) is conducted.Remarkably,the survey demonstrates remarkable efficiency,with 20 virtual tags swiftly deployed,each requiring a mere 2 s for precise positioning.Subsequently,these virtual tags are employed as seeds in a region-growing algorithm for point cloud segmentation.The accuracy of virtual tag positioning is found to be exceptional,with an average error of 2.4±1.8 cm.Importantly,the user experience is significantly enhanced,leading to improved seed positioning and,consequently,more accurate final segmentation results.

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。