A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source software

Abstract

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. © Springer Nature Switzerland AG 2018.