改进的布料模拟算法在多波束点云粗差剔除中的应用

针对目前多波束滤波需要人工剔除的现状，在布料模拟滤波算法（cloth simulation filtering，CSF）基础上，提出了一种基于点云分割和自适应参数调整改进的CSF算法。先对多波束点云数据进行分割，然后根据点云数据的面积和标准差调整每块CSF算法的参数，再构建布料滤波模型剔除粗差点，最终将分割后的点云拼接生成水下地形点云数据。结果表明，本文算法与CSF算法相比，克服了过度滤波现象，而且对于不同地区与人工方法粗差点剔除比分别从1.03下降到1.01和从1.46下降到1.19；与人工剔除粗差相比，避免了遗漏粗差点，人工干预很少。

Application of Improved Cloth Simulation Filtering in Eliminating Gross Errors of Multibeam Point Clouds

According to the current situation that multibeam filtering should be artificially eliminated, an improved cloth simulation filtering (CSF) based on point cloud segmentation and adaptive parameter adjustment was proposed. First of all, the multibeam point cloud data was segmented. Secondly, the parameters of each CSF algorithm were adjusted according to the area and standard deviation of the point cloud data. Then, the fabric filtering model was constructed to eliminate the gross error. Finally, the segmented point cloud was stitched to generate the underwater terrain point cloud data. The results show that the over-filtering defect is eliminated compared with the CSF algorithm, and the gross error elimination ratio is decreased from 1.03 to 1.01 and from 1.46 to 1.19 respectively for different regions. Compared with the manual elimination of gross error, the missed gross error can be avoided and little manual intervention is evolved.