共查询到18条相似文献，搜索用时 328 毫秒
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分带K-均值聚类的平面标靶定位 总被引：1，自引：0，他引：1
提出了一种分带K-均值聚类的平面标靶定位方法.根据标靶与测站距离的限制条件,推导了较大噪声点的剔除公式,在整体最小二乘拟合平面的基础上增加了噪声点二次剔除的方法,对经过噪声点剔除的点云数据进行分带、聚类处理.同时,对每一带的聚类中心进行均值化处理,得到每一带的中心点,通过求取不同带中心的均值来确定标靶中心点.实验结果表明,分带K-均值聚类的平面标靶定位模型较适合于平面标靶同名点确定. 相似文献
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朱宁宁 《测绘与空间地理信息》2015,(5)
针对目前LiDAR获取的点云数据配准中ICP算法迭代计算效率较低和使用标靶配准时精扫标靶费时费力等问题,提出利用扫描地物所包含的平面特征及点云数据的离散特性,通过拟合平面得到平面的单位法向量进行旋转角的求解.由于点云数据中含有误差,使用拟合平面的法向量不仅避免了对标靶的精细扫描,而且也消弱了点云误差对转换参数的影响.最后通过实例验证了本文方法的可行性与严密性. 相似文献
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针对常用的平面拟合方法在有"噪声点"存在的情况下,会出现拟合不稳定的问题,本文采用稳健性较好的RANSAC算法,从机载Li DAR数据中提取出建筑物顶部面片。RANSAC算法进行参数拟合时,会存在一些缺陷,通过改进RANSAC算法(LMed S算法)可以达到更好的拟合效果。首先利用直通滤波器对点云数据进行简单的滤波,然后通过Voxel Grid滤波器对点云数据进行下采样。对下采样之后的点云数据,用LMed S算法提取建筑物顶部面片。试验表明,利用LMed S算法可以成功提取建筑物顶部面片,稳健性较好。 相似文献
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针对点云平面拟合中存在粗差及异常值等问题,对结合特征值法的随机抽样一致性(random sample consensus,RANSAC)平面拟合算法进行了改进。该方法以RANSAC算法为基础,结合特征值法,利用点到平面模型距离的标准偏差来自动选取阈值t,通过阈值t检测并剔除异常数据点,达到获得理想平面拟合参数的目的。用改进的算法和传统的特征值法分别对点云数据进行处理,结果表明,改进的算法适用于存在误差和异常值的点云数据拟合,能稳定地获得较好的平面参数估值,具有较强的鲁棒性。 相似文献
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坐标转换是测绘领域经常遇到的问题,如局部坐标系和全局坐标系间的转换、多传感器融合时各传感器间坐标系的相互转换等等.采用平面代替传统坐标转换中的公共点,建立了基于平面的坐标转换模型.首先给出平面的标准定义;然后提出一种基于稳健的 RANSAC算法的特征值的平面拟合方法用于从点云数据中提取公共平面;构建基于平面表示的坐标转换模型并推导转换参数的计算方法;最后,通过模拟数据与实测数据验证模型的正确性. 相似文献
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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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The extraction of object features from massive unstructured point clouds with different local densities, especially in the presence of random noisy points, is not a trivial task even if that feature is a planar surface. Segmentation is the most important step in the feature extraction process. In practice, most segmentation approaches use geometrical information to segment the 3D point cloud. The features generally include the position of each point (X, Y and Z), locally estimated surface normals and residuals of best fitting surfaces; however, these features could be affected by noisy points and in consequence directly affect the segmentation results. Therefore, massive unstructured and noisy point clouds also lead to bad segmentation (over-segmentation, under-segmentation or no segmentation). While the RANSAC (random sample consensus) algorithm is effective in the presence of noise and outliers, it has two significant disadvantages, namely, its efficiency and the fact that the plane detected by RANSAC may not necessarily belong to the same object surface; that is, spurious surfaces may appear, especially in the case of parallel-gradual planar surfaces such as stairs. The innovative idea proposed in this paper is a modification for the RANSAC algorithm called Seq-NV-RANSAC. This algorithm checks the normal vector (NV) between the existing point clouds and the hypothesised RANSAC plane, which is created by three random points, under an intuitive threshold value. After extracting the first plane, this process is repeated sequentially (Seq) and automatically, until no planar surfaces can be extracted from the remaining points under the existing threshold value. This prevents the extraction of spurious surfaces, brings an improvement in quality to the computed attributes and increases the degree of automation of surface extraction. Thus the best fit is achieved for the real existing surfaces. 相似文献
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针对地形起伏较大的公路，水准测量断面效率低下的问题，本文运用RTK测量手段和分区平面拟合的方法来解决带状地形高程异常转换问题。依托CORS基站差分信号，得到平面精度为3 cm的纵断面放样和横断面采集数据。通过剔除法选择最优的转换控制点及分区平面拟合方式实现大地高转换为正常高，该方法减轻了外业作业强度，满足了公路断面测量精度，且最终高程转换中误差小于5 cm。 相似文献
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空间坐标转换在大地测量、工程测量等领域应用广泛．在利用公共点求解坐标转换参数时,针对公共点中混有多粗差点的情形,给出了基于罗德里格矩阵的坐标系转换模型,并在此基础上提出了基于随机抽样一致性(RANSAC)算法粗差剔除的坐标系转换抗差估计．最后利用仿真数据对该算法进行了验证,同时将该抗差算法与基于IGG3方案的最小二乘抗差估计算法进行了比较．算例结果表明,在20个仿真公共点数据中(仿真多组数据),当粗差点个数超过公共点总数的3／10时,基于IGG3方案的最小二乘抗差算法失效,而基于RANSAC的抗差算法在粗差点个数达到公共点总数的1／2时,依然能保证坐标转换的精度．该抗差算法将RANSAC算法的思想应用到坐标系转换上,有效地剔除了公共点中混有的大量粗差点. 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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针对目前传统图像匹配算法在复杂环境下存在误匹配点对过多、稳健性较差等问题，本文提出一种基于改进FAST的特征点提取，结合对立颜色特征的图像匹配算法。首先，利用改进FAST算法提取的角点作为特征点，结合改进的Opponent SIFT算法对特征点进行描述；然后，使用基于字符定位算法对提取的特征点对进行粗匹配，降低整体匹配过程中特征点对误匹配的风险。最后，为了规避因RANSAC算法易陷入局部最优解而导致正确点对被误剔除的问题，运用向量场一致性替代RANSAC进行提纯，降低误匹配率。通过对比试验表明，改进算法匹配准确率均大于91%，且对差异变化具有较好的稳健性、适应性。 相似文献