Abstract
This paper proposes a replicable methodology to enhance the accuracy of the photogrammetric reconstruction of large-scale objects based on the optimization of the procedures for Unmanned Aerial Vehicle (UAV) camera image acquisition. The relationships between the acquisition grid shapes, the acquisition grid geometric parameters (pitches, image rates, camera framing, flight heights), and the 3D photogrammetric surface reconstruction accuracy were studied. Ground Sampling Distance (GSD), the necessary number of photos to assure the desired overlapping, and the surface reconstruction accuracy were related to grid shapes, image rate, and camera framing at different flight heights. The established relationships allow to choose the best combination of grid shapes and acquisition grid geometric parameters to obtain the desired accuracy for the required GSD. This outcome was assessed by means of a case study related to the ancient arched brick Bridge of the Saracens in Adrano (Sicily, Italy). The reconstruction of the three-dimensional surfaces of this structure, obtained by the efficient Structure-From-Motion (SfM) algorithms of the commercial software Pix4Mapper, supported the study by validating it with experimental data. A comparison between the surface reconstruction with different acquisition grids at different flight heights and the measurements obtained with a 3D terrestrial laser and total station-theodolites allowed to evaluate the accuracy in terms of Euclidean distances.
Highlights
Automated photogrammetry using Unmanned Aerial Vehicle (UAV) image acquisition for digital surface reconstruction has become more widespread in recent years
A comparison between the surface reconstruction with different acquisition grids at different flight heights and the measurements obtained with a 3D terrestrial laser and total station-theodolites allowed to evaluate the accuracy in terms of Euclidean distances
The conducted analysis allowed us to check that all reconstructions contained errors
Summary
Automated photogrammetry using UAV image acquisition for digital surface reconstruction has become more widespread in recent years. This can be attributed to the enhanced performance of UAV [1,2,3] and to the development of different computer vision algorithms [4] and computational techniques, which have greatly speeded up the processing time and the quality of the reconstruction [5,6,7] These techniques have been used for different purposes, including shape detection [8,9] and 3D surface reconstruction of large-scale elements, where a high number of photos is necessary, such as natural environments and geographical configurations [10,11,12], buildings and urban textures [13,14,15], archaeological sites [16,17], and industrial installations [18,19]. When both the dimensions of the object being reconstructed and the accuracy increase, the computational time of the algorithms increases significantly, limiting them to high-speed reconstruction applications
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