Abstract
Lately, affordable unmanned aerial vehicle (UAV)-lidar systems have started to appear on the market, highlighting the need for methods facilitating proper verification of their accuracy. However, the dense point cloud produced by such systems makes the identification of individual points that could be used as reference points difficult. In this paper, we propose such a method utilizing accurately georeferenced targets covered with high-reflectivity foil, which can be easily extracted from the cloud; their centers can be determined and used for the calculation of the systematic shift of the lidar point cloud. Subsequently, the lidar point cloud is cleaned of such systematic shift and compared with a dense SfM point cloud, thus yielding the residual accuracy. We successfully applied this method to the evaluation of an affordable DJI ZENMUSE L1 scanner mounted on the UAV DJI Matrice 300 and found that the accuracies of this system (3.5 cm in all directions after removal of the global georeferencing error) are better than manufacturer-declared values (10/5 cm horizontal/vertical). However, evaluation of the color information revealed a relatively high (approx. 0.2 m) systematic shift.
Highlights
Remote sensing methods of mass data collection are increasingly used in the current technical and scientific practice
It can be reasonably expected that unmanned aerial vehicle (UAV) lidar systems will, in the future, find application at least in some of these fields as well
The principal step lies in the use of portable square targets (0.5 × 0.5 m) covered with tem overcoming the problem of identification of individual points within the cl a highly reflective foil facilitating their easy identification in the point cloud as well as step lies in the use of ofthe portable square targets
Summary
Remote sensing methods of mass data collection are increasingly used in the current technical and scientific practice These techniques, such as UAV or aerial photogrammetry, or 3D scanning (ground, mobile or airborne), provide point clouds, which are subsequently used for modeling of terrain or above-ground objects. SfM is presently used in many applications including volume determination [1,2], mining [3], forestry [4], determination of solar potential [5], terrain changes [6,7], natural hazards [8,9], etc It is, always necessary to consider the limitations of the methods associated with the suitable flight strategy [10,11,12] ground control points (GCPs) placement [13,14], processing algorithms [15], etc. It can be reasonably expected that UAV lidar systems will, in the future, find application at least in some of these fields as well
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