汪文琪, 李宗春, 付永健, 何华, 熊峰. 一种多尺度自适应点云坡度滤波算法[J]. 武汉大学学报 ( 信息科学版), 2022, 47(3): 438-446. DOI: 10.13203/j.whugis20200016
引用本文: 汪文琪, 李宗春, 付永健, 何华, 熊峰. 一种多尺度自适应点云坡度滤波算法[J]. 武汉大学学报 ( 信息科学版), 2022, 47(3): 438-446. DOI: 10.13203/j.whugis20200016
WANG Wenqi, LI Zongchun, FU Yongjian, HE Hua, XIONG Feng. A Multi-scale Adaptive Slope Filtering Algorithm for Point Cloud[J]. Geomatics and Information Science of Wuhan University, 2022, 47(3): 438-446. DOI: 10.13203/j.whugis20200016
Citation: WANG Wenqi, LI Zongchun, FU Yongjian, HE Hua, XIONG Feng. A Multi-scale Adaptive Slope Filtering Algorithm for Point Cloud[J]. Geomatics and Information Science of Wuhan University, 2022, 47(3): 438-446. DOI: 10.13203/j.whugis20200016

一种多尺度自适应点云坡度滤波算法

A Multi-scale Adaptive Slope Filtering Algorithm for Point Cloud

  • 摘要: 点云坡度滤波算法原理简单、易于实现,为进一步提升坡度滤波算法的自适应性,提出了一种多尺度自适应点云坡度滤波算法。首先,在数据预处理的基础上引入虚拟网格对点云数据进行分割;然后,利用距离加权的方式逐次计算网格点的坡度角,结合k均值聚类和正态分布自适应确定滤波阈值;最后,使用多尺度策略逐级缩小网格尺寸实现点云数据的精细滤波。采用两种密度不同的点云数据集对所提算法进行了验证,并将结果与两种坡度滤波算法及国际摄影测量和遥感学会提供的经典算法进行对比,实验表明所提算法整体滤波结果较好,稳定性更高,且适用于不同场景的点云数据。

     

    Abstract:
      Objectives  Point cloud filtering is the basic work of extracting ground information, generating digital elevation model and other terrain products. The slope filtering algorithm for point cloud is simple and easy to implement. In order to further improve the adaptability of slope filtering algorithm, a multi-scale adaptive slope filtering algorithm for point cloud is proposed.
      Methods  The idea of multi-scale pseudo-grid is used for reference, and the filtering threshold of grid is determined by combining k-means clustering and normal distribution adaptive to realize multi-scale adaptive point cloud slope filtering. Firstly, a pseudo-grid is introduced to divide the point cloud on the basis of data pre-processing. Then, the slope angle of grid points is calculated one by one using distance weighting, and the threshold value is determined adaptively by combining k-means clustering and normal distribution. Finally, the multi-scale strategy is used to reduce the grid size step by step to achieve fine filtering of point cloud data.
      Results  The algorithm is verified using two point cloud data sets with different densities. Compared with the two slope filtering algorithms and the classical algorithms of International Society for Photogrammetry and Remote Sensing, the experimental results show that this algorithm has better overall fitting results and higher stability.
      Conclusions  Through the above experiments, the proposed method can be applied to the filtering of point cloud data in different scenes.

     

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