The Impact of the Calibration Method on the Accuracy of Point Clouds Derived Using Unmanned Aerial Vehicle Multi-View Stereopsis

In unmanned aerial vehicle (UAV) photogrammetric surveys, the camera can be pre-calibrated or can be calibrated “on-the-job” using structure-from-motion and a self-calibrating bundle adjustment. This study investigates the impact on mapping accuracy of UAV photogrammetric survey blocks, the bundle adjustment and the 3D reconstruction process under a range of typical operating scenarios for centimetre-scale natural landform mapping (in this case, a coastal cliff). We demonstrate the sensitivity of the process to calibration procedures and the need for careful accuracy assessment. For this investigation, vertical (nadir or near-nadir) and oblique photography were collected with 80%–90% overlap and with accurately-surveyed (σ ≤ 2 mm) and densely-distributed ground control. This allowed various scenarios to be tested and the impact on mapping accuracy to be assessed. This paper presents the results of that investigation and provides guidelines that will assist with operational decisions regarding camera calibration and ground control for UAV photogrammetry. The results indicate that the use of either a robust pre-calibration or a robust self-calibration results in accurate model creation from vertical-only photography, and additional oblique photography may improve the results. The results indicate that if a dense array of high accuracy ground control points are deployed and the UAV photography includes both vertical and oblique images, then either a pre-calibration or an on-the-job self-calibration will yield reliable models (pre-calibration RMSEXY = 7.1 mm and on-the-job self-calibration RMSEXY = 3.2 mm). When oblique photography was Remote Sens. 2015, 7 11934 excluded from the on-the-job self-calibration solution, the accuracy of the model deteriorated (by 3.3 mm horizontally and 4.7 mm vertically). When the accuracy of the ground control was then degraded to replicate typical operational practice (σ = 22 mm), the accuracy of the model further deteriorated (e.g., on-the-job self-calibration RMSEXY went from 3.2–7.0 mm). Additionally, when the density of the ground control was reduced, the model accuracy also further deteriorated (e.g., on-the-job self-calibration RMSEXY went from 7.0–7.3 mm). However, our results do indicate that loss of accuracy due to sparse ground control can be mitigated by including oblique imagery.