# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 4 Experiments
### SuperGlue vs. ORB  
Compared to ORB and earlier approaches, we consider the SuperGlue to be arguably the next-generation detection and matching approach using neural networks. From this perspective, the ORB and SuperGlue tests result in two outcomes. The first one is about the detection and matching success rate, especially in the presence of a historical landmark dataset. SuperGlue is tested superior to ORB when the reference landmark features are generated from infrequently updated Google Maps aerial orthophotos. The higher number of matching features from SuperGlue results in better estimating the transformation matrix. This advantage vanishes when the reference landmarks are generated from a recent aerial mapping. Figure 14 shows the qualitative matching performance of SuperGlue vs. ORB. Quantitative results for more recently acquired landmark datasets are shown in Table 7. It can be observed that both SuperGlue and ORB work well and achieve similar accuracy in UAS geopositioning where the speed of error on average is 3.4 m/s. The predicted trajectories are visualized in Figure 15 and are compared with the reference trajectory (plotted in red). Yellow and blue trajectories almost overlap, which agrees with the quantitative analysis in Table 7.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**GIS Filter**} & \multirow{2}{*}{**Transformation**} & \multicolumn{4}{c}{**Residential Area**} & \multicolumn{4}{c}{**University Campus**} & \multicolumn{4}{c}{**Parking**} & \multicolumn{4}{c}{**Farm1**} \\ \cline{3-14}  & & **Min. ** & **Max. ** & **MAE** & **Std. ** & **Min. ** & **Max. ** & **MAE** & **Std. ** & **Min. ** & **Max. ** & **MAE** & **Std. ** & **Min. ** & **Max. ** & **MAE** & **Std. ** \\ \hline \multirow{2}{*}{Activated} & Pseudo-perspective & 0.0 & 12.3 & **2.9** & **3.10** & 0.0 & **6.4** & **1.8** & **1.53** & 2.0 & 6.1 & 4.5 & 0.70 & 0.0 & **5.3** & **1.8** & **1.25** \\  & Affine & 0.0 & 12.1 & 3.1 & 3.24 & 0.0 & 6.5 & **1.8** & 1.56 & 1.8 & 6.2 & 4.5 & 0.67 & 0.0 & 6.1 & 1.9 & 1.35 \\ \hline \multirow{2}{*}{Deactivated} & Pseudo-perspective & 0.0 & **12.0** & 3.4 & 3.40 & 0.0 & 29.5 & 6.2 & 8.28 & 2.1 & **6.0** & 4.5 & **0.66** & 0.0 & 5.5 & **1.8** & **1.25** \\  & Affine & 0.0 & 12.2 & 3.4 & 3.38 & 0.0 & 33.7 & 6.6 & 8.59 & **0.6** & 6.1 & **4.4** & 0.68 & 0.0 & 5.7 & 1 Figure 11: In horizontal space, four scenarios superimposed with GPS sensor readings during the flight are shown in red, the predicted geopositions using our approach in yellow and ORB-SLAM3 in monocular mode predicted geopositions in green. The reference and predicted start and end positions are the red pin icons. Vertical results can be seen in Figure 16.  
Figure 12: UAS reference geoposition (red) and estimated UAS geopositioning. The blue trajectory shows the GIS filter-activated trajectory, and the green shows the deactivated trajectory for pseudo-perspective transformation. The GIS filter significantly improves the predicted altitude in regions 1, 2 and 3. Another outcome of this experiment is the practical use of the detection and matching scheme for real-time edge computing applications. The ORB-based pipeline has a higher processing speed averaging 7 fps which is twice the speed of the SuperGlue-based pipeline. During the flight, the faster processing allows faster flight and a larger lever arm between consecutive image acquisitions (see Table 7 for details). Figure 14: Landmark matching performance of SuperGlue (**left**) vs. ORB (**right**). The aerial orthophoto used for the comparison is generated by aerial mapping. Figure 13: Visualization of the UAS vertical geopositioning from two perspective views on Google Earth, activating the GIS filter (red) vs. deactivating it (yellow). The regions 1\(\sim\)3 correspond to the same regions in Figure 12.  
Figure 15: Geopositioning test at a farm site (referred to _Farm2_ in Table 4), SuperGlue (yellow) vs. ORB (blue). The red is the reference trajectory from the platform GPS sensor.