On the Quality of Point-Clouds Derived from Sfm-Photogrammetry Applied to UAS Imagery

Structure from Motion photogrammetry (SfM-photogrammetry) recently appeared in environmental sciences as an impressive tool allowing for the creation of topographic data from unstructured imagery. Several authors have tested the performance of SfM-photogrammetry vs that of TLS or dGPS. Whilst the initial results were very promising, there is currently a growing awareness that systematic deformations occur in DEMs and point-clouds derived from SfM-photogrammetry. Notably, some authors have identified a systematic doming manifest as an increasing error vs distance to the model centre. Simulation studies have confirmed that this error is due to errors in the calibration of camera distortions. This work aims to further investigate these effects in the presence of real data. We start with a dataset of 220 images acquired from a sUAS. After obtaining an initial self-calibration of the camera lens with Agisoft Photoscan, our method consists in applying systematic perturbations to 2 key lens parameters: Focal length and the k1 distortion parameter. For each perturbation, a point-cloud was produced and compared to LiDAR data. After deriving the mean and standard deviation of the error residuals (ɛ), a 2nd order polynomial surface was fitted to the errors point-cloud and the peak ɛ defined as the mathematical extrema of this surface. The results are presented in figure 1. This figure shows that lens perturbations can induce a range of errors with systematic behaviours. Peak ɛ is primarily controlled by K1 with a secondary control exerted by the focal length. These results allow us to state that: To limit the peak ɛ to 10cm, the K1 parameter must be calibrated to within 0.00025 and the focal length to within 2.5 pixels (≈10 µm). This level of calibration accuracy can only be achieved with proper design of image acquisition and control network geometry. Our main point is therefore that SfM is not a bypass to a rigorous and well-informed photogrammetric approach. Users of SfM-photogrammetry will still require basic training and knowledge in the fundamentals of photogrammetry. This is especially true for applications where very small topographic changes need to be detected or where gradient-sensitive processes need to be modelled.