Mobile laser scanning (MLS) has attracted increasing interest in recent years for precise mapping of roads and highways. MLS data is used for road and road-side inventory analysis, where ground filtering serves as initial but important step. Therefore, we propose a hybrid ground filtering method, with four well-designed steps: point cloud partitioning, interquartile range-based ground segment identification, ground filtering in the labelled lowest clusters, and adaptive threshold-based ground point retrieval. The proposed method was tested and validated comprehensively using eight MLS datasets from all types of roadway environment. The ground points were filtered out in these data sets at average total error and kappa coefficient of 1.73% and 96.36%, respectively. The proposed method performs satisfactorily in the challenging specific cases of terrain features and achieves significant improvement in comparison with several state-of-the-art methods. The proposed method has potential for wider use in industry as it is straightforward and computationally efficient.

近年来，移动激光扫描（MLS）引起了人们对公路和高速公路精确地图绘制的越来越多的兴趣。MLS数据用于道路和路边库存分析，其中地面过滤是最初但重要的一步。因此，我们提出了一种混合的地面滤波方法，该方法具有四个精心设计的步骤：点云划分，基于四分位数范围的地面分段识别，标记的最低群集中的地面滤波以及基于自适应阈值的地面点检索。使用来自各种类型道路环境的八个MLS数据集对所提出的方法进行了全面的测试和验证。在这些数据集中，分别以平均总误差和kappa系数分别为1.73％和96.36％滤除了接地点。所提出的方法在具有挑战性的特定地形特征情况下表现令人满意，并且与几种最新方法相比，取得了显着改善。所提出的方法具有直接性和计算效率，因此具有在工业中广泛使用的潜力。