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1.
This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks. Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods. 相似文献
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室内平面要素的准确提取与关系恢复是室内模型自动化语义重建的重要基础,本文提出了一种面向复杂三维点云的室内平面要素提取与优化方法。该方法首先利用区域增长和RANSAC平面混合分割方法分割室内点云数据;其次利用室内语义部件的空间位置信息及包围盒和法向量信息,对分割后的平面进行平面要素的精确提取;然后对提取的墙面进行优化,实现共享墙面的合并,解决室内墙面冗余的问题;最后利用门与墙的空间位置信息,恢复门墙关联关系。试验部分采用了两组试验数据：一组是深圳大学某层教学楼的激光点云数据,另一组是国际摄影测量与遥感学会（ISPRS）的标准数据,通过对试验结果进行评估,验证了本文方法的有效性和可靠性。 相似文献
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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。 相似文献
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地面三维激光扫描是城市建筑立面数据采集的新方法,由于三维点云具有数据量大、无规则等特点,导致从点云中精确分割城市建筑物信息面临着严峻的挑战。在传统张量投票方法基础上,充分考虑各个尺度下点云立面特征判别的概率,提出了一种多尺度张量投票方法来实现平面分割,更加精确地实现了立面特征的识别。通过实例,将该方法同主成分分析和传统张量投票进行对比与分析,结果表明,多尺度张量投票方法在分割精度方面较优。 相似文献
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本文基于高速公路高精度点云数据，首先通过点云数据的分类处理实现对树木点云数据的提取，将树木点云投影到水平面，采用DBSCAN密度聚类算法实现单根树木的提取；然后在数据密集区域存在树木树冠点云重叠的区域，本文结合树干几何特征提取树干的位置信息，计算所有点云到树干中心的欧氏距离，将所有点云归类到最近的树干进行粗分割；最后根据粗分割的树木轮廓特征确定树冠模型与树冠中心，提出了采用基于密度特征的格网竞争算法对重叠的区域进行精细分割。试验表明，本文采用的树木分割方法能够实现单棵树木精确提取。 相似文献
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Roof plane segmentation is a complex task since point cloud data carry no connection information and do not provide any semantic characteristics of the underlying scanned surfaces. Point cloud density, complex roof profiles, and occlusion add another layer of complexity which often encounter in practice. In this article, we present a new technique that provides a better interpolation of roof regions where multiple surfaces intersect creating non-manifold points. As a result, these geometric features are preserved to achieve automated identification and segmentation of the roof planes from unstructured laser data. The proposed technique has been tested using the International Society for Photogrammetry and Remote Sensing benchmark and three Australian datasets, which differ in terrain, point density, building sizes, and vegetation. The qualitative and quantitative results show the robustness of the methodology and indicate that the proposed technique can eliminate vegetation and extract buildings as well as their non-occluding parts from the complex scenes at a high success rate for building detection (between 83.9% and 100% per-object completeness) and roof plane extraction (between 73.9% and 96% per-object completeness). The proposed method works more robustly than some existing methods in the presence of occlusion and low point sampling as indicated by the correctness of above 95% for all the datasets. 相似文献
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Nusret Demir 《Journal of the Indian Society of Remote Sensing》2018,46(8):1265-1272
The purpose of this study is to derive vectoral 3D roof planes from the LIDAR point cloud of the detected buildings. For segmentation of the LIDAR point cloud, the RANSAC algorithm has been used. Because the RANSAC algorithm is sensitive to the used parameters, and results in over- or under-segmentation of the clusters, a refinement method has been proposed. The detection of roof planes has been improved with use of the refinement method. Therefore, similar plane surfaces have been combined, followed by the region-growing algorithm, to split the under-segmented plane surfaces. The digitization of the roof boundaries is performed using the alpha-shapes algorithm, followed by line fitting to generalize the roof edges. The quality assessment has been done using the reference vector dataset with comparison using four different criteria. 相似文献
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针对在三维点云环境下分离目标物体所出现的过度分割问题,提出一种结合随机抽样一致性和颜色差值区域聚类的分割方法。首先利用RANSAC算法去除场景中大部分平面,使得目标物体和连成片的点云脱离,然后结合点云的距离阈值和目标颜色差值,得到目标点云数据。针对L1中值算法对曲率较大模型的骨架提取存在的不足,进行了改进。通过L1中值算法对点云模型进行骨架提取,得到点云的骨架点,然后沿端点方向向外进行最大内切球的球心提取,最后连接多个球心及骨架末端点,得到符合人类视觉效果的骨架。改进的算法提高了L1中值对曲率较大点云骨架提取的准确性。 相似文献
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一种基于ISS-SHOT特征的点云配准算法 总被引：2，自引：0，他引：2
针对点云配准过程中易产生错误对应点、收敛速度慢、配准时间长等问题，提出了一种基于内部形态描述子（ISS）及方向直方图描述子（SHOT）特征的点云配准算法。运用体素格网法下采样后，采用ISS算法提取特征点，并用SHOT对特征点进行描述，利用余弦相似度匹配对应点对，再采用RANSAC算法剔除错误对应点对，使得两片点云获得良好的初始位姿，最后采用点到平面的ICP算法进行精确配准。试验结果表明，与传统ICP算法及基于ISS的SAC-IA+ICP算法相比，本文算法配准精度及配准效率更高，对数据量大、重叠率较低点云具有很好的稳健性。 相似文献
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Laser scanning systems have been established as leading tools for the collection of high density three-dimensional data over physical surfaces. The collected point cloud does not provide semantic information about the characteristics of the scanned surfaces. Therefore, different processing techniques have been developed for the extraction of useful information from this data which could be applied for diverse civil, industrial, and military applications. Planar and linear/cylindrical features are among the most important primitive information to be extracted from laser scanning data, especially those collected in urban areas. This paper introduces a new approach for the identification, parameterization, and segmentation of these features from laser scanning data while considering the internal characteristics of the utilized point cloud – i.e., local point density variation and noise level in the dataset. In the first step of this approach, a Principal Component Analysis of the local neighborhood of individual points is implemented to identify the points that belong to planar and linear/cylindrical features and select their appropriate representation model. For the detected planar features, the segmentation attributes are then computed through an adaptive cylinder neighborhood definition. Two clustering approaches are then introduced to segment and extract individual planar features in the reconstructed parameter domain. For the linear/cylindrical features, their directional and positional parameters are utilized as the segmentation attributes. A sequential clustering technique is proposed to isolate the points which belong to individual linear/cylindrical features through directional and positional attribute subspaces. Experimental results from simulated and real datasets demonstrate the feasibility of the proposed approach for the extraction of planar and linear/cylindrical features from laser scanning data. 相似文献
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道路边界精确提取建模是城市道路管理、智能交通规划和高精度地图制作等领域的重要课题之一。本文提出了一种基于车载激光雷达点云数据和开源街道地图(OSM)的三维道路边界精确提取方法。首先,针对原始车载LiDAR点云数据应用布料模拟滤波分离地面点,再结合相对高程分析获取道路边界点候选数据集。然后,应用OSM矢量道路网数据的节点辅助道路边界点候选点集进行分段。最后,在各分段点云数据集中基于随机抽样一致性算法获得三维道路边界点集。通过直道、弯道及高密度复杂场景3种不同类型的城区道路边界路段分类提取试验。结果表明,利用该方法进行道路边界提取的准确率和召回率分别达96.12%和95.17%,F1值达92.11%,本文方法可用于高精度道路边界的三维精细提取与矢量化,进而为智能交通与无人驾驶导航提供支撑。 相似文献
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针对圆形有效反射区域的平面标靶拖尾点和因遮挡造成的数据缺失问题,提出一种基于RANSAC的残缺平面标靶稳健定位方法。文中采用RANSAC算法拟合标靶平面,使经过测距误差修正的反射点规整位于标靶平面;利用Givens变换将空间三维圆拟合简化为二维圆RANSAC拟合。采用两个实验分析同一平面标靶因不同遮挡对定位精度的影响。结果表明,该方法能够有效解决拖尾点和数据缺失问题,提高平面标靶定位的鲁棒性。 相似文献
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建筑物屋顶面点云分割结果的好坏对建筑物三维模型重建起着重要的作用。针对传统RANSAC算法建筑物屋顶面点云的分割问题,提出了一种基于局部约束的建筑物点云平面分割方法。利用点云局部曲面法向约束构建法向准则,利用半径约束的点云空间聚类的方法对共面屋顶面点云进行分解,从而抑制"伪屋顶面"的产生;利用局部抽样策略降低算法的迭代次数,减少运算量。实验表明该方法能够获得稳定可靠的建筑物屋顶点云分割结果,将有利于后续的建筑物三维模型重建。 相似文献
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面向室内弱纹理三维重建需求，本文以RGB-D摄影测量技术获取室内点云为基础，提出了四元组标靶辅助的点云配准方法。该方法首先通过阈值筛选大曲率点，自动识别邻接点云中的辅助标靶，然后采用随机采样一致性表达方法，拟合标靶参数及其中心坐标，并根据拟合参数匹配同名标靶中心，通过刚性转换完成邻接点云粗配准。在此基础上，迭代估算邻接点云间的重叠区域，优化点云间的配准参数，从而实现点云精配准。利用Kinect相机获取两类室内场景各12站点云对本文方法进行测试，试验结果表明，配准后的多站点云间距最大均方根误差优于一个采样间隔，证明了该方法在弱纹理室内点云配准中的可靠性。 相似文献
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针对现有大规模点云数据平面特征分割方法中存在的错误识别、效率低、抗噪性差等问题,该文提出一种基于2D霍夫变换和八叉树的建筑物平面精细分割方法。该方法首先,对原始点云进行空间均匀降采样并向X-Y面投影,利用改进的2D霍夫变换算法提取投影后的点云线段,使用选权迭代法精确计算线段所在直线的方程及端点坐标,进一步确定立面的空间几何方程;接下来,建立原始点云数据的八叉树结构,利用端点坐标设计立方体并分割出立方体内的立面点云;最后,将立面点云从原始点云中剔除,对余下点云降采样并向X-Z面投影,重复以上过程分割水平面点云。试验验证了该文方法对建筑物面状特征分割的有效性。 相似文献
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针对倾斜摄影场景中建筑物单体化问题,本文提出了基于倾斜摄影测量点云数据的建筑物识别和边界提取自动化算法。首先,对点云进行预处理,去除地面点和噪声点;然后,对点云进行二维栅格化处理,按间隔距离预分割;最后,结合改进的大津算法和区域增长算法,从预分割点云识别其中的建筑物,并提取建筑物边界点。从广东省江门市和湛江市选取两处试验区域对算法进行测试,结果表明:区域内建筑物点云均能准确被分割识别,建筑物边界提取准确度分别为87.8%与92.3%,说明本文提出的方法对于倾斜摄影测量建筑物识别和边界提取的适用性较强。 相似文献
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本文基于机器视觉探讨数字摄影测量三维构像下的智能数据处理要素之二：海量点云分割处理技术。多模型拟合方法通过将点云拟合到不同模型中,依照点云空间分布特征和几何结构特征进行分割。针对点云数据量巨大、分布不均匀、结构复杂等特性,本文提出一种基于多模型拟合的点云分割方法。首先通过降采样,采用基于密度分布的聚类方法,实现对点云的预分割。在预分割基础上,利用基于分裂合并的多模型拟合方法对点云进行后续拟合分割。针对平面和弧面,本文采用不同的拟合方式,最终实现对室内密集点云分割。试验结果表明,该方法能够在无须提前设置模型数目的情况下实现点云的自动分割。且相较于现有的点云分割技术,此方法相较于现今的常规方法能取得更好的分割效果,在分割的正确率上要高于现有的常规分割方法,在处理相同数据量的点云分割时,能够达到远低于常规方法的时间消耗。通过本文提出的三维点云分割方法能够实现将大规模、复杂三维点云数据分割为较为精细、具有准确模型参数的三维几何图元,为后续实现大规模、复杂场景的精确三维构象提供有力支持。 相似文献
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针对现有的LiDAR点云分割算法稳健性差、效率低的问题,本文提出了一种新的层次化聚类分割算法。该算法首先把点云生成自适应分辨率的超体素,然后以超体素为基元,改进成对链接的分割算法,实现三维点云的分割。试验结果表明,该分割算法与现有的分割方法相比,具有更好的稳健性和更高的计算效率,避免了点云过分割和欠分割的问题。本文算法在分割细节方面更加突出,分割结果可有效地保证后续数据处理工作的精度。 相似文献