A Point Cloud Filtering Algorithm for Combining Region Growth and Cloth Simulation
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.
point cloudfilteringregion growthcloth simulation filtering
NETL
NSTL
万方数据
