基于阈值自适应确定的多波束点云滤波算法

An adaptive threshold-based multi-beam point cloud filtering algorithm

针对水下多波束地形测量点云去噪问题,提出了一种基于阈值自适应确定的点云滤波算法。首先引入虚拟网格对多波束点云进行分区编号,并采用二次分层统计法剔除显著离群噪点;其次归一化计算网格内种子点与邻域各点的坡度角,引入k-Medoids聚类算法自适应更新坡度阈值;最后按照多尺度滤波窗口逐级对点云进行迭代运算,得到精细化地形点云。2个实验区的多波束点云滤波实验结果表明,本文算法能较好去除多波束点云的噪声和非地形点,有效提高坡度阈值的自适应性,滤波精度有明显提升,可以适用于大规模的多波束点自动化云滤波工作。

An adaptive threshold-based multi-beam point cloud filtering algorithm is proposed for point cloud denoising of underwater terrain multi-beam measurement.Firstly,the virtual grid is designed to number the multi-beam point clouds in different zones,and the significant outliers can be eliminated by using the quadratic stratified statistics method;Secondly,normalizing the gradient angle between the seed points in the grid and each point in the neighborhood,and k-Medoids clustering is introduced to update the slope threshold adaptively;Lastly,according to the multi-level filter window,the point cloud is iterated step by step to obtain the refined terrain point cloud.The experimental results in two experimental areas show that the proposed algorithm can effectively remove the noise and non-topographic points of multi-beam point clouds,effectively improve the adaptability of slope threshold,and significantly improve the filtering accuracy,which can be applied to largescale automatic cloud filtering of multi-beam points.