A curvature-based statistical method for generating DTM from LiDAR Point Cloud

Jianhua Wan, Ronggang Huang, Zhe Zeng, Shujuan Sun
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Abstract

The result of Skewness Balancing based on elevation(SBE) contains non-ground points, which involve low vegetation, vehicle, and side of buildings. According to this problem, this paper proposes an algorithm to improve the precision of automatic filter and reduce the commission error. The algorithm is Curvature-based Statistical Method (CSM), which is based on the Skewness Balancing, and introduces curvature into Skewness Balancing. Compared with the result of Skewness Balancing based on elevation, the experiment demonstrates that the algorithm performs better.
基于曲率的激光雷达点云DTM生成统计方法
基于高程的偏度平衡(SBE)结果包含非地点,包括低植被、车辆和建筑物侧面。针对这一问题,本文提出了一种提高自动滤波精度和减小调试误差的算法。该算法是基于偏度平衡的曲率统计方法(CSM),在偏度平衡中引入曲率。实验结果表明,该算法与基于仰角的偏度平衡算法相比,具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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