## 6 Conclusions and Future Steps
### AR Tracking and Visualization Present and Future Vision  
Work into the AR components--drone tracking, calibration, and visualization--of the presented solution is still ongoing. While the currently developed system is a working prototype, a number of possible future improvements have been outlined and planned for the medium term.  
Figure 14 presents our vision for such future additions.  
Regarding calibration, the working prototype uses a manual, two-step calibration, which can prove both tiresome and challenging for inexpert users. Hence, work is already in progress to calibrate initial drone pose with a largely automated, visual method. Two options are currently considered, as outlined in the Section 4.4: a visual drone pose estimation AI and the reading of QR codes pasted on the drones. The former should also provide intermittent visual recognition of the drone during flight, correcting the IMU readings and eliminating any accumulated drift.  
In addition, it can be noted that the position as tracked in AR will always lag some time behind that of the real drone, as IMU readings must be collected, read, and aggregated, a position estimated and forwarded to the HoloLens and there displayed. A future module could raw input from the flight control of the drone (pitch, roll, yaw, and throttle) and estimate a future position for the drone, offsetting the lag of data transmission and processing.  
Figure 14: Present and future components of the AR part of the solution. Solid lines indicate completed modules, dashed lines work in progress, and dotted lines future plans.  
Finally, the AR video canvas is currently displayed a set distance in front of the virtual (tracked) drone. However, the actual distance between the drone and the objects in its field of view might range from a couple of meters to hundreds. A depth estimation algorithm could gauge this distance and position the video canvas appropriately, for a more realistic display in context with the environment.  
Conceptualization, K.K., G.A. and A.D.; data curation, D.T. and G.A.; formal analysis, K.K. and D.T.; funding acquisition, A.D. and P.D.; investigation, K.K. and D.T.; methodology, K.K. and K.C.; project administration, A.D. and P.D.; resources, A.D. and P.D.; software, K.C., D.T. and D.S.; supervision, K.K., A.D. and P.D.; validation, K.K.; visualization, D.S.; writing--original draft, K.K., K.C., D.T., D.S. and G.A.; writing--review and editing, K.K., K.C., A.D. and P.D. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the European Commission within the context of the project FASTER, funded under EU H2020 Grant Agreement 833507.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors would like to thank the Hellenic Rescue Team Attica (HRTA--Greece) and the Ecole Nationale Superieure des Officiers de Sapeurs-Pompiers (ENSOSP--France) for testing the proposed system in the context of search-and-rescue operations and providing valuable feedback. Additional thanks are due to drone operators [PERSON] and [PERSON] for testing the gesture control system during its development and offering feedback from an experienced operator's point of view.  
The authors declare no conflict of interest.