# Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation
## Results and Discussion  
### Influence of the Standard Deviation Assigned to the Observations  
The results of varying the standard deviation of the camera coordinates on the accuracy of block orientation in the **RTK** configuration are shown in the graphs of Figure 2, where the RMSE for the horizontal coordinates and for the elevation has been computed on all the 26 available checkpoints. Note that the standard deviation value read on the abscissa is always the one assigned to the XY coordinates of a particular BBA, as well as for the plots referring to the Z coordinate. The variety of the trends from program to program and from test flight to test flight is large. As the XY and Z graphs do not necessarily show their minimum at the same value of standard deviation, an optimization of the standard deviation should be performed for XY and Z coordinates, as GNSS measurements are less accurate in height than in latitude and longitude. Overall, with all packages the  
Figure 2: RMSE of the horizontal coordinates (**left**) and of the height (**right**) at 26 checkpoints for the four flights (RTK1 to RTK4) and for the three software packages as a function of the standard deviation assigned to the camera projection centers. Note that the range of the RMSE in each graph is different and that the standard deviation value on the abscissa is the one assigned to the XY coordinates of a particular BBA also for the RMSE Z plots. RMSE is barely affected by the precision assigned to the horizontal coordinates (especially in MicMac), though generally the larger the standard deviation the larger the error. With PhotoScan, however, in one case (the RTK1 flight) the RMSE range in XY is larger than 3 cm. The accuracy in elevation, to the contrary, is strongly affected by changes in the standard deviation assigned to the camera Z coordinate: the RMSE range is up to 8 cm with PhotoScan and Pix4D and much larger for MicMac. A common trend is hard to identify, even for the same package: in some cases, the RMSE shows a well-defined minimum, in some other it decreases with increasing standard deviation, in some other the opposite happens. As far as the software packages are concerned, with PhotoScan the best results in Z are obtained for standard deviations of 2-4 cm in RTK1 and RTK4; then they deteriorate with larger standard deviations. In the other two cases, though there is no clear minimum, larger standard deviations produce smaller errors. In Pix4D in three cases the Z error improves the larger the standard deviation; only in RTK4 there is a clear minimum at around 3 cm of standard deviation. In all cases an RMSE in Z of 4 cm or better is reached. MicMac is almost insensitive to changes in the horizontal coordinate's standard deviation in the whole range from 1 to 10 cm. In Z, three types of behavior are apparent. In RTK2 and RTK4, the error drops from 25 to 35 cm at 1 cm standard deviation to a clear minimum around 3.5 cm; then, the RMSE grows slowly and steadily (asymptotically) towards a constant error (of 8 and 5 cm respectively). In RTK1, the error decreases monotonically towards a constant error of 2 cm, while, in RTK3, there is a minimum at 1.5 cm and then an asymptotic increase to about 8 cm. Looking for hints on how to operate practically, inner quality measurements such as the mean reprojection error are useless (or even wrong) guides in the search for such optimum. In principle the smaller the standard deviation assigned to the camera stations in the BBA, the more the solution will be constrained, so the reprojection error should increase. On the contrary, if the standard deviations are large, the contribution of camera station observations gets smaller, i.e., they will not affect the residuals of collinearity equations. In practice, however, the variations of the reprojection error will be very small, even if mm level standard deviations are assigned to the camera stations (see Table A1 in Appendix A).