Error control in UAV image acquisitions for 3D reconstruction of extensive architectures

This work describes a simple, fast, and robust method for identifying, checking and managing the overlapping image keypoints for 3D reconstruction of large objects with numerous geometric singularities and multiple features at different lighting levels. In particular a precision 3D reconstruction of an extensive architecture captured by aerial digital photogrammetry using Unmanned Aerial Vehicles (UAV) is developed. The method was experimentally applied to survey and reconstruct the ‘Saraceni’ Bridge’ at Adrano (Sicily), a valuable example of Roman architecture in brick of historical/cultural interest. The variety of features and different lighting levels required robust self-correlation techniques which would recognise features sometimes even smaller than a pixel in the digital images so as to automatically identify the keypoints necessary for image overlapping and 3D reconstruction. Feature Based Matching (FBM) was used for the low lighting areas like the intrados and the inner arch surfaces, and Area Based Matching (ABM) was used in conjunction to capture the sides and upper surfaces of the bridge. Applying SIFT (Scale Invariant Feature Transform) algorithm during capture helped find distinct features invariant to position, scale and rotation as well as robust for the affinity transformations (changes in scale, rotation, size and position) and lighting variations which are particularly effective in image overlapping. Errors were compared with surveys by total station theodolites, GPS and laser systems. The method can facilitate reconstruction of the most difficult to access parts like the arch intrados and the bridge cavities with high correlation indices.