# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 5 Conclusions  
In this work, we have introduced a real-time positioning pipeline that can provide absolute 3D space geopositioning and works better than state-of-the-art approaches. In this approach, the UAS can fly in extensive geospatial coverage and change attitude and altitude without losing vertical and horizontal accuracy. To achieve this performance, we introduce a fast geospatial database containing geo-tagged and labeled landmarks and an attitude control mechanism to mitigate matching problems due to platform motion.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Min. 1** & **Max. 1** & **MAE 1** & **Std 1** & **fps 2** & **dist 3** & **Max. Speed 4** \\ \hline SG 5 & 1.0 & 5.1 & 3.4 & 1.08 & 3.40 & 33 & 112 \\ ORB & 1.0 & 5.1 & 3.4 & 1.06 & 6.58 & 90 & 592 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Measures of UAS geopositioning accuracy, meter(s). 2 UAS Geopositioning pipeline average running speed, f/s. 3 Allowed maximum distance between two consecutive frames, m/f. 4 Max. speed = fps \(\times\) gap, m/s. Theoretically, this is the allowed maximum speed under the promise of ensuring the pipeline run in real time. 5 SuperGlue.  
\({}^{\dagger}\) SupersGlue.  
\end{table}
Table 7: UAS geopositioning pipeline landmark matching scheme (SuperGlue vs. ORB) comparison on horizontal geopositioning accuracy, pipeline inference speed, and allowed UAS maximum speed for high-end GPUs.  
Figure 16: Horizontally, we compare our approach (blue trajectory) with monocular ORB-SLAM3 (green trajectory) and the reference altimeter readings (red trajectory). Under each scenario, the horizontal comparisons are shown in Figure 16.  
The pipeline can be extendable and contain other modules. We included a GIS filtering module that improves vertical errors to demonstrate this. Compared with recent SLAM methods, our approach provides real-time absolute geoposition and does not suffer from drift problems.  
Conceptualization, J.W. and A.Y.; methodology, J.W. and A.Y.; software, J.W.; data curation, J.W.; writing--original draft preparation, J.W.; writing--review and editing, J.W. and A.Y.; visualization, J.W.; supervision, A.Y.; project administration, A.Y.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The source code and data presented in our experiments are available on request from the corresponding author. Source code is available at [[https://github.com/JianliWei1995/UAS-Visual-Geopositioning](https://github.com/JianliWei1995/UAS-Visual-Geopositioning)]([https://github.com/JianliWei1995/UAS-Visual-Geopositioning](https://github.com/JianliWei1995/UAS-Visual-Geopositioning)).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & Convolutional Neural Network \\ GCP & Ground Control Point \\ GIS & Geographic Information System \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ IMU & Inertial Measurement Unit \\ ORB & Oriented FAST and Rotated BRIEF \\ SLAM & Simultaneous Localization and Mapping \\ UAS & Unmanned Aerial System \\ \end{tabular}