Abstract
Point clouds derived using structure from motion (SfM) algorithms from unmanned aerial vehicles (UAVs) are increasingly used in civil engineering practice. This includes areas such as (vegetated) rock outcrops or faces above linear constructions (e.g., railways) where accurate terrain identification, i.e., ground filtering, is highly difficult but, at the same time, important for safety management. In this paper, we evaluated the performance of standard geometrical ground filtering algorithms (a progressive morphological filter (PMF), a simple morphological filter (SMRF) or a cloth simulation filter (CSF)) and a structural filter, CANUPO (CAractérisation de NUages de POints), for ground identification in a point cloud derived by SfM from UAV imagery in such an area (a railway ledge and the adjacent rock face). The performance was evaluated both in the original position and after levelling the point cloud (its transformation into the horizontal plane). The poor results of geometrical filters (total errors of approximately 6–60% with PMF performing the worst) and a mediocre result of CANUPO (approximately 4%) led us to combine these complementary approaches, yielding total errors of 1.2% (CANUPO+SMRF) and 0.9% (CANUPO+CSF). This new technique could represent an excellent solution for ground filtering of high-density point clouds of such steep vegetated areas that can be well-used, for example, in civil engineering practice.
Highlights
In engineering practice, unmanned aerial vehicles (UAVs) equipped with cameras are increasingly used for the acquisition of 3D data
The combination was beneficial as these filters are in a wayat complementary; the CANUPO filter performs well in removing low vegetation while the geometrical filter is highly successful in removing the tree trunks and crowns that are at a higher distance from the ground
As the testing results indicate, these filters are principally unsuitable for steep rugged objects: the PMF filter yielded a total error of 60%, SMRF of
Summary
In engineering practice, unmanned aerial vehicles (UAVs) equipped with cameras are increasingly used for the acquisition of 3D data. It is often difficult to acquire detailed data of specific terrain profiles such as steep rugged rock terrain covered with vegetation Such terrain is common in the vicinity of linear structures (railways, roads, etc.) as well as in natural areas including protected areas in the mountains and other nature reserves. Such steep rugged terrain can be pleasing to the eye, it can pose a major risk to safety This is true for the aforementioned linear constructions where objects falling from such slopes can cause potentially lethal accidents not to mention the financial damage (e.g., a train derailed due to a boulder on the track). Safety measures (e.g., netting preventing objects from falling) are widely employed
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