# An Aerial Mixed-Reality Environment for First-Person-View Drone Flying
## 5 Conclusions and Future Work  
We proposed a flight environment that enabled easy flight control via a wide viewing angle and a stereoscopic view that was safe from drone collisions using virtual obstacles mixed with the real world, instead of actual obstacles. Drone racing flags used to configure the flight environment of a drone were learned and identified using a faster R-CNN. To mix the 2D coordinates of identified objects with a virtual environment, they were mapped three dimensionally to virtual world coordinates, and an appropriate method of creating virtual object locations was investigated. For a qualitative evaluation, test driving was performed by expert pilots using this method, and the results confirmed that the FPV MR drone flight did not cause simulator sickness. The user immersion was surveyed through comparison of cinematic experiences with existing real FPV and virtual simulator FPV drone flights, and the results showed that the immersion of the proposed method improved. The two-sample \(t\)-test showed that there was a statistically significant difference between the two groups regarding user immersion at the 95% level.  
The proposed FPV MR flight provided drone pilots with an experience to improve immersion in the flight environment, and provided positive assistance to the FPV drone flight of non-experts. Furthermore, the bidirectional MR interactions between real and virtual worlds can be useful for cutting-edge unmanned aerial vehicles for entertainment. In the experiments conducted in this study, the streaming video from the drone maintained a speed of 30 fps, and the overall speed of the framework was maintained at 50-70 fps. However, object recognition in each frame was limited because, not only must object recognition be performed in the user's VR backpack, but also mixed reality must be created and rendered in the VR HMD. The proposed method was performed in the background of Unity3D because the object recognition speed was lower than the appropriate speed of 20 ms. Furthermore, each of the frames for which object recognition was not performed was interpolated to minimize the tremor or incorrect matching of virtual objects.  
In the future, we will study and apply more efficient object recognition and tracking techniques to increase real-time object recognition, and construct an efficient MR environment. Furthermore, we will conduct more comprehensive research focused on user experience, and extend our study to a fully mobile MR environment using wireless communication.  
Conceptualization, D.-H.K. and S.-M.C.; methodology D.-H.K. and Y.-G.G.; software, D.-H.K. and Y.-G.G.; validation, D.-H.K.; formal analysis, D.-H.K.; investigation, D.-H.K.; resources, S.-M.C.; data curation, D.-H.K. and Y.-G.G.; writing--original draft preparation, D.-H.K., writing--review and editing, D.-H.K. and S.-M.C.; visualization D.-H.K.; supervision, S.-M.C.; project administration, S.-M.C.; funding acquisition, S.-M.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2016-0-00312) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
The authors declare no conflict of interest.