计算机科学 ›› 2023, Vol. 50 ›› Issue (1): 156-165.doi: 10.11896/jsjkx.211100183
孟华儒, 吴国伟
MENG Huaru, WU Guowei
摘要: 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。
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