Terrestrial laser scanning has become an important surveying technique in many fields such as natural hazard assessment. To analyse earth surface processes, it is useful to generate a digital terrain model originated from laser scan point cloud data. To determine the terrain surface as precisely as possible, it is often necessary to filter out points that do not represent the terrain surface. Examples are vegetation, vehicles, and animals. In mountainous terrain with a small-structured topography, filtering is very difficult. Here, automatic filtering solutions usually designed for airborne laser scan data often lead to unsatisfactory results. In this work, we further develop an existing approach for automated filtering of terrestrial laser scan data, which is based on the assumption that no other surface point can be located in the area above a direct line of sight between scanner and another measured point. By taking into account several environmental variables and a repetitive calculation method, the modified method leads to significantly better results. The root-mean-square-error (RSME) for the same test measurement area could be reduced from 5.284 to 1.610. In addition, a new approach for filtering and interpolation of terrestrial laser scanning data is presented using a grid with horizontal and vertical angular data and the measurement length.
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