A Structure-from-Motion Pipeline for Topographic Reconstructions Using Unmanned Aerial Vehicles and Open Source Software
In recent years, the generation of accurate topographic reconstructions has found applications ranging from geomorphic sciences to remote sensing and urban planning, among others. The production of high resolution, high-quality digital elevation models (DEMs) requires a significant investment in personnel time, hardware, and software. Photogrammetry offers clear advantages over other methods of collecting geomatic information. Airborne cameras can cover large areas more quickly than ground survey techniques, and the generated Photogrammetry-based DEMs often have higher resolution than models produced with other remote sensing methods such as LIDAR (Laser Imaging Detection and Ranging) or RADAR (radar detection and ranging).
In this work, we introduce a Structure from Motion (SfM) pipeline using Unmanned Aerial Vehicles (UAVs) for generating DEMs for performing topographic reconstructions and assessing the microtopography of a terrain. SfM is a computer vision technique that consists in estimating the 3D coordinates of many points in a scene using two or more 2D images acquired from different positions. By identifying common points in the images both the camera position (motion) and the 3D locations of the points (structure) are obtained. The output from an SfM stage is a sparse point cloud in a local XYZ coordinate system. We edit the obtained point in MeshLab to remove unwanted points, such as those from vehicles, roofs, and vegetation. We scale the XYZ point clouds using Ground Control Points (GCP) and GPS information. This process enables georeferenced metric measurements. For the experimental verification, we reconstructed a terrain suitable for subsequent analysis using GIS software. Encouraging results show that our approach is highly cost-effective, providing a means for generating high-quality, low-cost DEMs.
KeywordsGeomatics Structure from Motion Open source software
This work has been partly funded by Universidad Tecnológica de Bolívar project (FI2006T2001). E. Sierra thanks Universidad Tecnológica de Bolívar for a Masters degree scholarship.
- 1.Nelson, A., Reuter, H., Gessler, P.: DEM production methods and sources. Dev. Soil Sci. 33, 65–85 (2009)Google Scholar
- 4.James, M., Robson, S.: Straightforward reconstruction of 3D surfaces and topography with a camera: accuracy and geoscience application. J. Geophys. Res. Earth Surf. 117(F3) (2012)Google Scholar
- 6.Goesele, M., Curless, B., Seitz, S.M.: Multi-view stereo revisited. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009, pp. 2402–2409. IEEE (2006)Google Scholar
- 7.Agisoft photoscan professional. http://www.agisoft.com/downloads/installer/
- 8.Pix4D. https://pix4d.com/
- 9.Mapillary: OpenSfM. https://github.com/mapillary/OpenSfM
- 10.OpenDroneMap. https://github.com/OpenDroneMap/OpenDroneMap
- 11.Bradski, G., Kaehler, A.: OpenCV. Dr. Dobb’s J. Softw. Tools. 3 (2000)Google Scholar
- 12.Altizure. https://www.altizure.com
- 13.Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G.: MeshLab: an open-source mesh processing tool. In: Eurographics Italian Chapter Conference, vol. 2008, pp. 129–136 (2008)Google Scholar
- 14.Duane, C.B.: Close-range camera calibration. Photogram. Eng 37(8), 855–866 (1971)Google Scholar
- 15.Google maps. https://maps.google.com
- 17.Bolick, L., Harguess, J.: A study of the effects of degraded imagery on tactical 3D model generation using structure-from-motion. In: Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XIII, vol. 9828, p. 98280F. International Society for Optics and Photonics (2016)Google Scholar
- 18.Grauman, K., Leibe, B.: Visual object recognition. In: Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 5, no. 2, pp. 1–181 (2011)Google Scholar
- 20.Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP (1) 2(331–340), 2 (2009)Google Scholar
- 23.Adorjan, M.: “openSfM ein kollaboratives structure-from-motion system”; betreuer/in (nen): M. wimmer, m. birsak; institut für computergraphik und algorithmen. abschlussprüfung: 02.05.2016 (2016)Google Scholar