基于无人机的道路点云数据分割提取算法

针对机载LiDAR获得道路的数据信息精确度低问题,提出基于无人机的低空扫描三维点云数据,动态拟合提取分割道路信息的算法.首先使用主成分分析法获得道路点数据的法向量,之后将高程信息和法向量信息结合,利用聚类算法获得道路的高程和法向量的范围,提取道路点云数据;其次利用多项式拟合对道路数据进行数学建模;然后通过动态多项式拟合提取出所有路面数据和路面上的资产以及行人车辆数据;最后使用区域生长算法对路面上的资产以及行人车辆数据进行分割.实验表明算法对道路上的遮挡物有很强的抗干扰能力,可以将路面提取出来并将路面上的数据分割进行分割,将本文算法与区域生长算法进行对比,本文算法对路面数据更加敏感.     

基于RANSAC的公路路面坑槽提取方法

针对基于三维激光点云的坑槽扫描提取算法计算量大、效率低的问题,提出基于RANSAC思想的坑槽提取方法.首先,使用RANSAC计算横断面基准线,矫正横断面数据并初步识别坑槽点及其位置;其次,对坑槽区域使用RANSAC计算坑槽局部基准路面,由此标记出坑槽点及路面点;然后使用种子填充算法进行连通域求解,计算出坑槽点集;最后对...     

保留路面特征的车载激光点云非均匀压缩方法

针对车载移动测量系统获取的海量路面点云数据的压缩问题,提出一种根据激光点高程和强度信息设计的路面点云非均匀压缩法。该方法基于点云密度和路面目标地物尺寸确定点抽稀和线抽稀尺度,按扫描线索引进行单条扫描线上激光点抽稀和多条扫描线抽稀;通过分析扫描线上窗口内激光点的强度和高程分布判断目标点是否为信息特征点,并予以保留。实验表明,该方法在点云数据压缩时能有效保留路面标识线、坑槽、修补等地物的边缘特征点。     

基于双判定因子的道路绿化带分割

以高速公路的无人机影像点云数据为研究对象, 提出一种基于双判定因子的道路绿化带分割算法. 首先对点云数据进行串行下采样, 在降低点云数目的同时尽可能多地保留点云特征点; 其次, 对降采样后的点云数据进行正射影校正; 最后, 提出一种结合法向量夹角与 RANSAC 平面分割双判定的点云分割算法, 实现了对高速公路中绿化带的准确分割, 采用绿化带边界提取算法最终实现高速公路环境信息的分割. 以G85高速凤翔段的无人机影像点云作为实验数据, 分别采用本文算法、基于法向量夹角的分割算法、基于RANSAC平面拟合分割算法进行验证. 实验结果表明基于双判定因子的道路绿化带分割算法对环境噪点及离群点有较好的抗干扰性, 可以有效过滤路面高曲率点, 提取结果较好.     

基于二次特征提取的煤矿巷道表面点云数据精简方法

采用三维激光扫描技术提取的煤矿巷道表面点云数据量大且存在较多的冗余数据,而现有点云数据精简方法存在大数量级点云处理过程中细节保留不足的问题。针对上述问题,提出了一种基于二次特征提取的煤矿巷道表面点云数据精简方法。首先对采集到的原始巷道点云数据进行去噪预处理;其次建立K-d树,并利用主成分分析法对去噪后点云数据估算来拟合邻域平面的法向量;然后通过较小的法向量夹角阈值对点云进行初步的特征区域与非特征区域划分,保留特征区域并随机下采样非特征区域,接着依据较大的法向量夹角阈值将特征区域点云划分为特征点和非特征点,并对非特征点进行体素随机采样;最后将2次点云精简结果与特征点合并得到最终的精简数据。仿真结果表明,该方法在百万数据量级点云和高精简率条件下,相较曲率精简方法、随机精简方法和栅格精简方法,在特征保留和重构精度方面都取得了更好的效果,三维重构后计算所得标准偏差平均可低于相同精简率下其他方法 30%左右。     

地面激光点云阶层式分类方法EI北大核心CSCD

针对不同地物之间点云特征的多样性和树木点云分布的无规律性,导致一般方法分类精度低的问题,提出一种基于对象的地面激光点云阶层式分类方法.首先采用欧氏距离聚类法将非地面点云分割;然后提出一种法向散乱系数计算方法,并用于树木的提取;最后结合点云对象的点个数、高程均值和平面拟合残差特征实现其他地物的分类.实验结果表明,该方法能有效地将复杂地物分类,相比于投影点密度法和支持向量机法分类精度更高.     

地面激光点云阶层式分类方法

针对不同地物之间点云特征的多样性和树木点云分布的无规律性,导致一般方法分类精度低的问题,提出一种基于对象的地面激光点云阶层式分类方法.首先采用欧氏距离聚类法将非地面点云分割;然后提出一种法向散乱系数计算方法,并用于树木的提取;最后结合点云对象的点个数、高程均值和平面拟合残差特征实现其他地物的分类.实验结果表明,该方法能有效地将复杂地物分类,相比于投影点密度法和支持向量机法分类精度更高.     

基于改进法向量估计的多视角点云配准算法

针对多视角三维测量中多片点云重叠区域提取及高精度配准的问题,本文提出一种多视角异源低重叠率点云配准方法。首先基于点云之间的初始位置,互相计算源点云和目标点云彼此的最近点集,自动提取两片点云重叠部分;然后使用迭代最近点算法精配准重叠点云。通过法向量特征进一步提高点云配准精度,并提出改进点云法向量估计算法用以剔除错误匹配点对,显著减小了复杂结构点云配准的距离均方根误差。结果表明,使用经典点云数据仿真实验验证了该算法的性能,并通过多视角条纹投影三维测量系统采集点云数据验证了算法的有效性。     

基于区域特征距离加权的三维地形建模方法

为了提高三维虚拟场景中三维地形真实感效果,提出了基于区域特征的距离加权的三维地形建模方法。首先,根据采样点数据的高程值对采样点数据进行分类,建立分类数据与插值点数量映射关系;然后,结合Diamond-square细分法求取插值点坐标数据,求得距离加权因子;最后,通过判断插值点的区域特征建立距离加权计算方程,以保证插值点间的平滑性和连贯性。理论分析和仿真结果表明,与传统地形建模方法相比,该方法能够提高三维地形的真实感,同时地形绘制速度提高20%。     

基于全向声呐的引水隧洞点云模型去噪方法研究

三维重建技术逐渐成为引水隧洞运营期安全检测的关键手段。而受隧洞特殊水文环境噪声、数据采集设备噪声以及载体运动噪声等影响,采集的点云数据不可避免的会遭受到噪声干扰,导致有用信息缺乏,不利于三维重建的进行。因此,该文提出了基于声呐数据特征点的点云去噪算法,实现隧洞点云数据的去噪。首先,该文依据引水隧洞声呐点云数据的特点,定义视觉距离和视角向量特征参数;其次,通过耦合视角向量与点云法向量估计点云漂移向量,并使用核函数方法估计视角距离参数的概率密度分布从而计算漂移距离;最后,采用漂移算法在保持点云模型特征的同时实现噪声的滤波。实验结果表明,该文提出的算法在去除隧洞点云模型数据噪声的同时能很好的保持引水隧洞模型的细节特征,为后续隧洞病害的检测提供高精度点云数据模型。     

Road pothole extraction and safety evaluation by integration of point cloud and images derived from mobile mapping sensors

The automatic detection and extraction of road pothole distress is an important issue regarding healthy road structures, monitoring, and maintenance. In this paper, a new algorithm that integrates the mobile point cloud and images is proposed for the detection of road potholes. The algorithm includes three steps: 2D candidate pothole extraction from the images using a deep learning method, 3D candidate pothole extraction via a point cloud, and pothole determination by depth analysis. Because the texture features of the pothole and asphalt or concrete patches greatly differ from those of a normal road, pothole or patch distress images are used to establish a training set and train and test the deep learning system. Subsequently, the 2D candidate pothole is extracted from the images and labeled via the trained DeepLabv3+, a state-of-the-art pixel-wise classification (semantic segmentation) network. The edge of the candidate pothole in the image is then used to establish the relationship between the mobile point cloud and images. The original road point cloud around the edge of the candidate pothole is categorized into two groups, that is, interior and exterior points, according to the relationship between the point cloud and images. The exterior points are used to fit the road plane and calculate the accurate 3D shape of the candidate potholes. Finally, the interior points of a candidate pothole are used to analyze the depth distribution to determine if the candidate pothole is a pothole or patch. To verify the proposed method, two cases, including real and simulation cases, are selected. The real case is an expressway in Shanghai with a length of 26.4 km. Based on the proposed method, 77 candidate potholes are extracted by the DeepLabv3+ system; 49 potholes and 28 patches are finally filtered. The affected lanes and pothole locations are analyzed. The simulation case is selected to verify the geometric accuracy of the detected potholes. The results show that the mean accuracy of the detected potholes is ∼1.5–2.8 cm.     

Visual analysis of asphalt pavement for detection and localization of potholes

Identifying and restoring distresses in asphalt pavement have key significance in durability and long life of roads and highways. A vast number of accidents occurs on the roads and highways due to the pavement distresses. This paper aims to detect and localize one of the critical roadway distresses, the potholes, using computer vision. We have processed images of asphalt pavement for experimentation containing the pothole and non-pothole regions. We proposed a top-down scheme for the detection and localization of potholes in the pavement images. First, we classified pothole/non-pothole images using a bag of words (BoW) approach. We employed and computed famous scale-invariant feature transform (SIFT) features to establish the visual vocabulary of words to represent pavement surface. Support vector machine (SVM) is employed for the training and testing of histograms of words of pavement images. Secondly, we proposed graph cut segmentation scheme to localize the potholes in the labelled pothole images. This paper presents both, subjective and objective evaluation of potholes localization results with the ground truth. We evaluated the proposed scheme on a pavement surface dataset containing the wide-ranging pavement images in different scenarios. Experimentation results show that we achieved an accuracy of 95.7% for the identification of pothole images with significant precision and recall. Subjective evaluation of potholes localization results in high recall with relatively good accuracy. However, the objective assessment shows the 91.4% accuracy for localization of potholes.     

Pothole detection in asphalt pavement images

Pavement condition assessment is essential when developing road network maintenance programs. In practice, the data collection process is to a large extent automated. However, pavement distress detection (cracks, potholes, etc.) is mostly performed manually, which is labor-intensive and time-consuming. Existing methods either rely on complete 3D surface reconstruction, which comes along with high equipment and computation costs, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In this paper we present a method for automated pothole detection in asphalt pavement images. In the proposed method an image is first segmented into defect and non-defect regions using histogram shape-based thresholding. Based on the geometric properties of a defect region the potential pothole shape is approximated utilizing morphological thinning and elliptic regression. Subsequently, the texture inside a potential defect shape is extracted and compared with the texture of the surrounding non-defect pavement in order to determine if the region of interest represents an actual pothole. This methodology has been implemented in a MATLAB prototype, trained and tested on 120 pavement images. The results show that this method can detect potholes in asphalt pavement images with reasonable accuracy.     

可变半径Alpha Shapes提取机载LiDAR点云建筑物轮廓

目的 机载激光雷达（light detection and ranging,LiDAR）能够快速获取建筑物表面的3维点云,为提取建筑物轮廓提供重要的数据支撑,但由于激光脚点的随机性和点云自身的离散性,常规固定半径Alpha Shapes（A-Shapes）算法难以兼顾轮廓提取的精细度和完整度,且在点数量较大情况下计算效率较低。因此,提出一种基于网格的可变半径Alpha Shapes方法用于提取机载LiDAR点云建筑物轮廓。方法 对3维点云进行投影降维,对投影后2维离散点的范围构建规则格网,接着根据网格内点云填充情况筛选出边界网格,计算边界网格的平滑度并加权不同的滚动圆半径,再以边界网格为中心生成3×3邻域网格检测窗口,利用滚动圆原理提取窗口内点集的边界点,迭代检测直到所有边界网格遍历完成,最后获取点云的完整轮廓。结果 在精度评价实验中,与固定半径A-Shapes方法和可变半径Alpha Shapes（variable radius Alpha Shapes,VA-Shapes）方法相比,若建筑物以直线特征为主且边缘点云参差不齐,则本文方法的提取效果不理想;若建筑物含有较多拐角特征,则本文方法的提取效果较好。在效率评价实验中,与A-Shapes方法、VA-Shapse方法以及包裹圆方法相比,若点云数据量较小,则4种方法的耗时差距不大;若数据量较大,则本文方法和包裹圆方法的耗时远小于固定半径A-Shapes方法。实验结果表明,本文提出的轮廓提取方法适用于多种形状的建筑物点云。从轮廓完整性、几何精度以及计算效率等几方面综合考虑,本文方法提取建筑物点云轮廓效果较好。结论 本文提出的基于网格的可变半径Alpha Shapes建筑物点云轮廓提取方法结合了网格划分和滚动圆检测的优点,能够有效提取机载LiDAR建筑物点云顶部轮廓,具有较高的提取效率和良好的鲁棒性,提取的轮廓精度较高。     

Point cloud simplification for the boundary preservation based on extracted four features

This paper proposes a simplification algorithm based on four feature parameters, aiming at solving the problem that the edge features cannot be retained due to the incompletely extracted sharp features during point cloud simplification. Firstly, K neighborhood searching is carried out for point cloud, and K neighborhood points are quickly found by a dynamic grid method. Then, four features including: the curvature of the point, the average of the normal angle of a point from a neighborhood point, the average distance between the point and the neighborhood point and the distance between the point and the center of gravity of the neighborhood point, are calculated according to the K neighborhood of the data point. The four parameters are used to define the feature discrimination parameters and feature thresholds, to compare the size and extract the feature points; finally, the non-feature points are reduced twice by the method of the bounding box, and the reduced point cloud and feature points are spliced to achieve the purpose of simplification. The experimental results show that the distance between the point and the center of gravity of the neighborhood has a great influence on the simplified model boundary, which effectively guarantees the accuracy of the simplified model.     

基于多判别参数混合方法的散乱点云特征提取

针对以往散乱点云特征提取算法存在尖锐特征点提取不完整以及无法保留模型边界点的问题,提出了一种多个判别参数混合方法的特征提取算法。首先,对点云构建k-d tree,利用k-d tree建立点云k邻域;然后,针对每个k邻域计算数据点曲率、点法向与邻域点法向夹角的平均值、点到邻域重心的距离、点到邻域点的平均距离;最后,据此四个参数定义特征阈值和特征判别参数,特征判别参数大于阈值的点即为特征点。实验结果表明,与已有算法相比,该算法不仅可以有效提取尖锐特征点,而且能够识别边界点。     

一种基于降采样后关键点优化的点云配准方法

针对工件点云数据多而导致点云配准耗时长的问题,提出一种基于降采样后关键点优化的点云配准方法。计算点云若干体素的重心,利用kd-tree快速遍历重心的邻近点来代替该体素;提出自适应的点云平均距离计算方法,对降采样后的点云提取ISS3D关键点,并采用基于球邻域的边界点判断方法对其优化;对优化后的关键点进行FPFH特征描述,利用SAC-IA求解近似变换阵,使用ICP算法精配准而解得工件的精确位姿信息。实验结果表明,相较于其他四种配准算法,配准精度分别提高了96.9%、98.1%、93.3%和3.5%,配准速度分别提高了77.2%、77.7%、76.9%和85.4%,表明了该方法的有效性。     

Fog removal using laser beam penetration,laser intensity,and geometrical features for 3D measurements in fog-filled room

Three dimension (3D) point cloud data in fog-filled environments were measured using light detection and ranging (LIDAR). Disaster response robots cannot easily navigate through such environments because this data contain false data and distance errors caused by fog. We propose a method for recognizing and removing fog based on 3D point cloud features and a distance correction method for reducing measurement errors. Laser intensity and geometrical features are used to recognize false data. However, these features are not sufficient to measure a 3D point cloud in fog-filled environments with 6 and 2 m visibility, as misjudgments occur. To reduce misjudgment, laser beam penetration features were added. Support vector machine (SVM) and K-nearest neighbor (KNN) are used to classify point cloud data into ‘fog’ and ‘objects.’ We evaluated our method in heavy fog (6 and 2 m visibility). SVM has a better F-measure than KNN; it is higher than 90% in heavy fog (6 and 2 m visibility). The distance error correction method reduces distance errors in 3D point cloud data by a maximum of 4.6%. A 3D point cloud was successfully measured using LIDAR in a fog-filled environment. Our method’s recall (90.1%) and F-measure (79.4%) confirmed its robustness.