一种联合区域生长和布料模拟的点云滤波算法
A Point Cloud Filtering Algorithm for Combining Region Growth and Cloth Simulation
黄绪洲 1赵建虎1
作者信息
- 1. 武汉大学测绘学院,湖北武汉,430079
- 折叠
摘要
现有的点云滤波算法大多数是基于点的,所考虑的点云信息较少,参数设置复杂,对低矮地物的识别能力不足,滤波精度不是太高.本文提出了一种联合区域生长和布料模拟的点云滤波算法.首先对点云数据进行预处理,滤除数据中的离群点和大多数植被点.然后通过改进的区域生长算法进行点云聚类形成一个个点云对象.同时使用布料模拟滤波算法获取得到初始地表.最后基于生成的点云对象对初始地表进行修正,获得最终滤波结果.实验结果表明,本文提出的改进算法抗差性更强,能更好的识别低矮地物,滤除非地面点,保留地面点,滤波精度相对于初始的布料模拟滤波算法有了明显提升.
Abstract
Most existing point cloud filtering algorithms are point-based and lack consideration of comprehensive point cloud information. They tend to involve complex parameters and exhibit limited capability in identifying low objects,ulti-mately resulting in suboptimal filtering precision. For this rea-son,this paper proposes a point cloud filtering algorithm that combines region growth and cloth simulation. In the algo-rithm,the data are first preprocessed to filter out outliers and most of vegetation points. Then,the point cloud is clustered to form point cloud objects by the improved region growing al-gorithm. Concurrently,the cloth simulation filtering algo-rithm is used to derive the initial ground surface. Finally,based on the point cloud objects,the initial ground surface is corrected to obtain the final filtering results. The experimental results demonstrate that the improved algorithm exhibits en-hanced robustness. It excels in recognizing low objects,filter-ing out non-ground points,preserving ground points,and sig-nificantly improving filtering precision in comparison to the ini-tial cloth simulation filtering algorithm.
关键词
点云/滤波/区域生长/布料模拟滤波Key words
point cloud/filtering/region growth/cloth simulation filtering引用本文复制引用
基金项目
国家重点研发计划(2022YFC2808303)
国家自然科学基金(42176186)
出版年
2024
NETL
NSTL
万方数据