Application of WPA-CSFTC model in point cloud filtering
In order to improve the point cloud filtering accuracy,adaptability,and stability of the classic cloth simulation filtering (CSF) algorithm,this paper proposed a CSF algorithm based on the wolf pack algorithm (WPA) and topography cognition. The improved filtering algorithm achieved point cloud filtering by following steps:firstly,the constructed topography cognition model was expanded into a rough digital elevation model (R-DEM);secondly,by normalizing point cloud topography,topography trends were separated from topography details;finally,the CSF algorithm optimized by WPA was used in point cloud filtering. The experiment was conducted using measured airborne laser point cloud data,and the accuracy of the filtering results was evaluated using error evaluation criteria and Kappa coefficients. The results show that the total point cloud filtering error of the WPA-CSFTC model is reduced by 6.13％ and 9.67％ compared to that of the classical CSF algorithm and CSFTC algorithm,respectively. The Kappa coefficients are increased by 23.60％ and 10.36％ compared to those of the classic CSF algorithm and CSFTC algorithm,respectively,and the classification effect for point clouds is better. It has high stability and adaptability in point cloud filtering.
point cloud filteringwolf pack algorithm (WPA)cloth simulation filtering (CSF)topography cognition model
万方数据
维普
