To attenuate positional errors of LiDAR-derived datasets for constructing digital elevation models (DEMs), a feature-preserving point denoising algorithm (F-PDA) is developed in this paper. F-PDA includes three main steps: surface normal estimation, normal filtering and point position update. Numerical tests with two simulated surfaces indicate that F-PDA is always more accurate than kriging and natural neighbour. Furthermore, F-PDA has a high effectiveness of preserving feature lines. Real-world examples of interpolating LiDAR samples demonstrate that F-PDA can best retain both prominent and subtle terrain features, while faithfully removing errors in mountainous and flat regions. Moreover, it outperforms some well-known interpolation methods.

为了减轻LiDAR数据集的位置误差以构建数字高程模型（DEM），本文开发了一种特征保留点去噪算法（F-PDA）。F-PDA包括三个主要步骤：表面法线估计，法线滤波和点位置更新。用两个模拟表面进行的数值测试表明，F-PDA始终比克里金法和自然邻居法更准确。此外，F-PDA在保留要素线方面具有很高的效率。内插LiDAR样本的真实示例表明，F-PDA可以最好地保留突出的和微妙的地形特征，同时忠实地消除山区和平坦地区的误差。此外，它的性能优于某些众所周知的插值方法。