Abstract
Abstract. The current work investigates the potential of two low-cost off-the-shelf quadcopters for multi-view reconstruction of sub-vertical rock faces. The two platforms used are a DJI Phantom 1 equipped with a Gopro Hero 3+ Black and a DJI Phantom 3 Professional with integrated camera. The study area is a small sub-vertical rock face. Several flights were performed with both cameras set in time-lapse mode. Hence, images were taken automatically but the flights were performed manually as the investigated rock face is very irregular which required manual adjustment of the yaw and roll for optimal coverage. The digital images were processed with commercial SfM software packages. Several processing settings were investigated in order to find out the one providing the most accurate 3D reconstruction of the rock face. To this aim, all 3D models produced with both platforms are compared to a point cloud obtained with a terrestrial laser scanner. Firstly, the difference between the use of coded ground control targets and the use of natural features was studied. Coded targets generally provide the best accuracy, but they need to be placed on the surface, which is not always possible, as sub-vertical rock faces are not easily accessible. Nevertheless, natural features can provide a good alternative if wisely chosen as shown in this work. Secondly, the influence of using fixed interior orientation parameters or self-calibration was investigated. The results show that, in the case of the used sensors and camera networks, self-calibration provides better results. To support such empirical finding, a numerical investigation using a Monte Carlo simulation was performed.
Highlights
RPAS, known as drones or UAVs, have been used in military applications for many years
SingleCamera Self-calibration (SCS) is used for the comparisons conducted using different ground control points (GCPs)
To summarise the entire simulation workflow: (i) in a first stage the pseudo-random algorithm assigns tiepoints, on the basis of 700 uniformly distributed ground points, to the different images considering the lower (10) and upper (20) limit specified by the user; (ii) the 7 GCP are projected on all the available images; (iii) all image coordinates are summed with a random normal error (σ = 0.5 pixel); (iv) the upper-rightmost GCP object coordinates are changed systematically, moving the points 3 cm in a horizontal direction parallel to the slope front; (v) the observation system is solved using the CALGE Bundle Block Adjustment (BBA) considering a SCS or Multi-Camera Selfcalibration (MCS) onthe-job calibration routine
Summary
RPAS, known as drones or UAVs, have been used in military applications for many years. Changing regulations, such as the one in Australia where by the end of this year everyone will be allowed to operate RPAS with mass less than 2 kg without a specific license, make such platforms even more attractive It is still not clear how such platforms can be used most efficiently to get the required accuracy for photogrammetric surveys. There is still a need to develop simple guidelines for the use of low-cost RPAS surveys in order to obtain accurate and reliable results. Low-cost platforms, instead, come with light-weight cameras with optics made of plastic and sensors using a rolling shutter (Chabok, 2013) They are generally not suitable for accurate photogrammetric surveys. A numerical investigation using a Monte Carlo simulation was developed to support the findings
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