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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Robust Locally Weighted Regression Techniques for Ground Surface Points Filtering in Mobile Laser Scanning Three Dimensional Point Cloud Data
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Robust Locally Weighted Regression Techniques for Ground Surface Points Filtering in Mobile Laser Scanning Three Dimensional Point Cloud Data

机译：用于移动激光扫描三维点云数据的地表点滤波的鲁棒局部加权回归技术

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摘要

This paper introduces robust algorithms for extracting the ground points in laser scanning 3-D point cloud data. Global polynomial functions have been used for filtering algorithms for point cloud data; however, it is not suitable as it may lead to bias for the filtering algorithms and can cause misclassification errors when many different objects are present. In this paper, robust statistical approaches are coupled with locally weighted 2-D regression that fits without any predefined global function for the variables of interest. Algorithms are performed iteratively on 2-D profiles: and . The (elevation) values are robustly down weighted based on the residuals for the fitted points. The new set of down-weighted values, along with the corresponding (or ) values, is used to get a new fit for the lower surface level. The process of fitting and down weighting continues until the difference between two consecutive fits is insignificant. The final fit is the required ground level, and the ground surface points are those that fall within the ground level and the level after adding some threshold value with the ground level for values. Experimental results are compared with the recently proposed segmentation method through simulated and real mobile laser scanning point clouds from urban areas that include many objects that appear in road scenes such as short walls, large buildings, electric poles, signposts, and cars. Results show that the proposed robust- methods efficiently extract ground surface points with better than 97% accuracy.
机译：本文介绍了用于提取激光扫描3-D点云数据中地面点的鲁棒算法。全局多项式函数已用于过滤点云数据的算法；但是，它不合适，因为它可能导致过滤算法出现偏差，并且当存在许多不同的对象时会导致分类错误。在本文中，稳健的统计方法与局部加权的二维回归相结合，该拟合无需对目标变量进行任何预定义的全局函数即可拟合。在2-D轮廓上迭代执行算法：和。基于拟合点的残差，稳健地降低（海拔）值的权重。新的一组减权值以及相应的（或）值可用于下表面水平的新拟合。拟合和向下加权的过程一直持续到两个连续拟合之间的差异不明显为止。最终拟合为所需的地面高度，地面点是落入地面以及在地面上加上一些阈值的值之后的地面点。通过模拟和真实的来自市区的移动激光扫描点云将实验结果与最近提出的分割方法进行了比较，该点云包括出现在道路场景中的许多对象，例如短墙，大型建筑物，电线杆，路标和汽车。结果表明，所提出的鲁棒方法能够以高于97％的精度有效地提取地面点。

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