Advanced 3D Photogrammetric Surface Reconstruction of Extensive Objects by UAV Camera Image Acquisition

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.

[1]  S. Campana.  Drones in Archaeology. State‐of‐the‐art and Future Perspectives , 2017 .

[2]  G. Sequenzia,et al.  Error control in UAV image acquisitions for 3D reconstruction of extensive architectures , 2017 .

[3]  Toby P. Breckon,et al.  Real-time people and vehicle detection from UAV imagery , 2011, Electronic Imaging.

[4]  Andrea Fusiello,et al.  Improving the efficiency of hierarchical structure-and-motion , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Nikolay Strigul,et al.  Embedded, real-time UAV control for improved, image-based 3D scene reconstruction , 2016 .

[6]  Eleni Mangina,et al.  State of technology review of civilian UAVS , 2016 .

[7]  Rick S. Blum,et al.  Matching of images with projective distortion using transform invariant low-rank textures , 2016, Journal of Visual Communication and Image Representation.

[8]  Changchang Wu,et al.  Towards Linear-Time Incremental Structure from Motion , 2013, 2013 International Conference on 3D Vision.

[9]  Cristina Bignardi,et al.  Structural analysis of skeletal body elements: numerical and experimental methods , 2009 .

[10]  Richard I. Hartley,et al.  A Fast Optimal Algorithm for L 2 Triangulation , 2007, ACCV.

[11]  Richard Szeliski,et al.  Symmetric Sub-Pixel Stereo Matching , 2002, ECCV.

[12]  Duke M. Bulanon,et al.  Comparison of Small Unmanned Aerial Vehicles Performance Using Image Processing , 2016, J. Imaging.

[13]  Marc Pollefeys,et al.  Autonomous Visual Mapping and Exploration With a Micro Aerial Vehicle , 2014, J. Field Robotics.

[14]  Nong Cheng,et al.  Autonomous navigation and environment modeling for MAVs in 3-D enclosed industrial environments , 2013, Comput. Ind..

[15]  Marco Dubbini,et al.  Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments , 2013, Remote. Sens..

[16]  Pascal Monasse,et al.  Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion , 2013, ICCV.

[17]  D. Girardeau-Montaut,et al.  Facets : a Cloudcompare Plugin to Extract Geological Planes from Unstructured 3d Point Clouds , 2016 .

[18]  Cecilio Angulo,et al.  Real-time video stabilization without phantom movements for micro aerial vehicles , 2014, EURASIP J. Image Video Process..

[19]  C. Strecha,et al.  A GENERATIVE MODEL FOR TRUE ORTHORECTIFICATION , 2008 .

[20]  Tobias Höllerer,et al.  Optimizing the Viewing Graph for Structure-from-Motion , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[21]  Heiko Hirschmüller,et al.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Debra F. Laefer,et al.  Maximizing feature detection in aerial unmanned aerial vehicle datasets , 2017 .

[23]  Salim Chikhi,et al.  An ear biometric system based on artificial bees and the scale invariant feature transform , 2016, Expert Syst. Appl..

[24]  Junwei Han,et al.  A Survey on Object Detection in Optical Remote Sensing Images , 2016, ArXiv.

[25]  Jianyu Huang,et al.  Rapid 3D Reconstruction for Image Sequence Acquired from UAV Camera , 2018, Sensors.

[26]  Debra F. Laefer,et al.  Flight Optimization Algorithms for Aerial LiDAR Capture for Urban Infrastructure Model Generation , 2009 .

[27]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[28]  Pascal Fua,et al.  On benchmarking camera calibration and multi-view stereo for high resolution imagery , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  T. Rabbani,et al.  Automatic reconstruction of industrial installations using point clouds and images , 2006 .

[30]  Ming Li,et al.  Rapid Texture Optimization of Three-Dimensional Urban Model Based on Oblique Images , 2017, Sensors.

[31]  Pablo J. Zarco-Tejada,et al.  Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods , 2014 .

[32]  Long Quan,et al.  Relative 3D Reconstruction Using Multiple Uncalibrated Images , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[33]  George Pavlidis,et al.  Multi-image 3D reconstruction data evaluation , 2014 .

[34]  Frank Dellaert,et al.  Structure from motion without correspondence , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[35]  Jian Xu,et al.  A UAV Image and Geographic Data Integration Processing Method and Its Applications , 2017, ICCIS 2017.

[36]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[37]  Amol D. Vibhute,et al.  EVALUATION OF PARTIALLY OVERLAPPING 3D POINT CLOUD'S REGISTRATION BY USING ICP VARIANT AND CLOUDCOMPARE , 2014 .

[38]  Henri Eisenbeiss,et al.  UAVS FOR THE DOCUMENTATION OF ARCHAEOLOGICAL EXCAVATIONS , 2010 .

[39]  Ye Zhang,et al.  Image processing based proposed drone for detecting and controlling street crimes , 2017, 2017 IEEE 17th International Conference on Communication Technology (ICCT).

[40]  Paul A. Beardsley,et al.  3D Model Acquisition from Extended Image Sequences , 1996, ECCV.

[41]  James M. Rehg,et al.  Adaptive Structure from Motion with a Contrario Model Estimation , 2012, ACCV.

[42]  Mani Golparvar-Fard,et al.  Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): a review of related works , 2016 .

[43]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[44]  Andrew Owens,et al.  Discrete-continuous optimization for large-scale structure from motion , 2011, CVPR 2011.