Cost-effective non-metric photogrammetry from consumer-grade sUAS: implications for direct georeferencing of structure from motion photogrammetry

Abstract

The declining costs of small Unmanned Aerial Systems (sUAS), in combination with Structure-from-Motion (SfM) photogrammetry have triggered renewed interest in image-based topography reconstruction. However, the potential uptake of sUAS-based topography is limited by the need for ground control acquired with expensive survey equipment. Direct georeferencing (DG) is a workflow that obviates ground control and uses only the camera positions to georeference the SfM results. However, the absence of ground control poses significant challenges in terms of the data quality of the final geospatial outputs. Notably, it is generally accepted that ground control is required to georeference, refine the camera calibration parameters, and remove any artefacts of optical distortion from the topographic model. Here, we present an examination of DG carried out with low-cost consumer-grade sUAS. We begin with a study of surface deformations resulting from systematic perturbations of the radial lens distortion parameters. We then test a number of flight patterns and develop a novel error quantification method to assess the outcomes. Our perturbation analysis shows that there exists families of predictable equifinal solutions of K1-K2 which minimize doming in the output model. The equifinal solutions can be expressed as K2 = f (K1) and they have been observed for both the DJI Inspire 1 and Phantom 3 sUAS platforms. This equifinality relationship can be used as an external reliability check of the self-calibration and allow a DG workflow to produce topography exempt of non-affine deformations and with random errors of 0.1% of the flying height, linear offsets below 10 m and off-vertical tilts below 1°. Whilst not yet of survey-grade quality, these results demonstrate that low-cost sUAS are capable of producing reliable topography products without recourse to expensive survey equipment and we argue that direct georeferencing and low-cost sUAS could transform survey practices in both academic and commercial disciplines.