基于地形认知的布料模拟滤波算法

Cloth Simulation Filtering Algorithm with Topography Cognition

数字高程模型(Digital Elevation Model,DEM)可以反映一个地区的地形特征,具有广泛的科研应用。对激光雷达点云数据进行点云滤波以提取地面点,并对地面点进行插值是构建DEM的常用步骤,其中在点云滤波过程中使用的滤波算法直接影响到最终构建的DEM的精度。布料模拟滤波(Cloth Simulation Filtering,CSF)算法作为一种点云滤波算法,具有模型简单、滤波效率高等优点,其针对平坦地区的滤波精度较高,但在处理复杂地形时会因布料模型的内部弹力以及重力惯性等因素,导致滤波结果的精度较差。为了提升CSF算法在处理复杂地形时的滤波精度和地形适应性,提高其构建DEM的精度,提出了基于地形认知的布料模拟滤波算法(Cloth Simulation Filtering Algorithm with Topography Cognition,CSFTC)。该算法提出了地形认知模型,基于点云数据点的局部分布特征构建认知模型,并将其扩展为粗精度数字高程模型(Rough Digital Elevation Mo-del,R-DEM);通过点云地形归一化实现宏观地形趋势与微观地形细节的分离;最终使用经典CSF算法结合R-DEM实现了点云滤波。文中设计了CSFTC算法与经典CSF算法的对比实验,CSFTC算法的平均总误差率从9.30%下降到5.10%,平均Ⅱ类误差率从30.02%下降到8.46%。实验结果表明,与经典CSF算法相比,CSFTC算法在平坦地区的滤波精度小幅上升,对复杂地形的滤波精度明显上升,提升了算法的地形适应性;Ⅱ类误差显著下降有助于提高构建的DEM的精度。

Digital elevation model(DEM)can reflect the topographic characteristics of an area and has a wide range of scientific research applications.Filtering LIDAR point cloud data,extracting the ground points and interpolating are common steps in constructing DEM.The filtering algorithm used in the process of point cloud filtering directly affects the accuracy of the final DEM.As a point cloud filtering algorithm,cloth simulation filtering(CSF)algorithm has the advantages of simple model and high filtering efficiency.It has high filtering accuracy for flat areas.However,when dealing with complex terrain areas,the accuracy of filtering results will be poor due to the internal elasticity and gravity inertia of the cloth model.In view of this,in order to improve the filtering accuracy and terrain adaptability of CSF algorithm in dealing with complex terrain areas,so as to improve the accuracy of constructing DEM,the cloth simulation filtering algorithm with terrain cognition(CSFTC)is proposed.The algorithm proposes a terrain-cognitive model.Based on the local distribution characteristics of point cloud data points,the terrain-cognitive model is constructed and extended to rough digital elevation model(R-DEM),which realizes the separation of macro terrain trend and micro terrain details through point cloud terrain normalization.Finally,the original CSF algorithm combined with R-DEM is used to realize point cloud filtering.Comparison experiment between CSFTC algorithm and the original CSF algorithm is designed.The average total error rate decreases from 9.30% to 5.10%,and the average type-Ⅱ error rate decreases from 30.02% to 8.46%.Experimental results show that compared with the original CSF algorithm,the accuracy of CSFTC algorithm increases slightly in flat region and increases significantly in complex region,which improves the terrain adaptability of the algorithm.The significant decrease of type-Ⅱ error is helpful to improve the accuracy of constructed DEM.