Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data
Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data
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DOI：
10.1109/tgrs.2016.2551546
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发表时间：
2016-05
影响因子：
8.2
通讯作者：
A. Nurunnabi;D. Belton;G. West
中科院分区：
文献类型：
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作者：
A. Nurunnabi;D. Belton;G. West
针对激光扫描三维点云数据，研究了曲面分割中的野值和噪声问题，提出了一种统计鲁棒的曲面分割算法。基于主成分分析（PCA）的局部显著性特征，例如，法线和曲率，已被频繁地以多种方式用于点云分割。然而，PCA是敏感的离群值;显着特征的PCA是不稳定和不准确的离群值的存在，因此，分割结果可能是错误的和不可靠的。作为一种补救措施，稳健的技术，例如，已经提出了随机样本一致性（RANSAC）和/或PCA的鲁棒版本（RPCA）。然而，RANSAC受到众所周知的淹没效应的影响，并且RPCA方法对于点云处理来说是计算密集型的。我们提出了一种基于区域生长的鲁棒分割算法，该算法使用最近推出的最大一致性与最小距离的鲁棒诊断PCA（RDPCA）方法来获得鲁棒的显著性特征。使用合成和激光扫描数据集的实验表明，基于RDPCA的方法具有处理离群和/或噪声污染的数据的内在能力。一个合成的数据集的结果表明，RDPCA是105倍的速度比RPCA，并给出更准确和更强大的结果时，与其他分割方法相比。与基于RANSAC和RPCA的方法相比，RDPCA与RANSAC的时间几乎相同，但RANSAC的结果明显不如RPCA和RDPCA的结果。结合分段合并算法，该方法是有效的庞大的点云数据组成的复杂物体表面从移动的，陆地和航空激光扫描系统。
This paper investigates the problems of outliers and/or noise in surface segmentation and proposes a statistically robust segmentation algorithm for laser scanning 3-D point cloud data. Principal component analysis (PCA)-based local saliency features, e.g., normal and curvature, have been frequently used in many ways for point cloud segmentation. However, PCA is sensitive to outliers; saliency features from PCA are nonrobust and inaccurate in the presence of outliers; consequently, segmentation results can be erroneous and unreliable. As a remedy, robust techniques, e.g., RANdom SAmple Consensus (RANSAC), and/or robust versions of PCA (RPCA) have been proposed. However, RANSAC is influenced by the well-known swamping effect, and RPCA methods are computationally intensive for point cloud processing. We propose a region growing based robust segmentation algorithm that uses a recently introduced maximum consistency with minimum distance based robust diagnostic PCA (RDPCA) approach to get robust saliency features. Experiments using synthetic and laser scanning data sets show that the RDPCA-based method has an intrinsic ability to deal with outlier- and/or noise-contaminated data. Results for a synthetic data set show that RDPCA is 105 times faster than RPCA and gives more accurate and robust results when compared with other segmentation methods. Compared with RANSAC and RPCA based methods, RDPCA takes almost the same time as RANSAC, but RANSAC results are markedly worse than RPCA and RDPCA results. Coupled with a segment merging algorithm, the proposed method is efficient for huge volumes of point cloud data consisting of complex objects surfaces from mobile, terrestrial, and aerial laser scanning systems.