# Autonomous Environment Generator for UAV-Based Simulation
## 5 Experimental Results and Discussion
Though aesthetically boring, this provides everything necessary for UAV interaction, as emphasized by Figure 16a,b. Our efficient simulation algorithm provides an outlet for UAV companies to test their products in a zero stakes environment, without expensive resources. UAVs can collide with the obstacles, and the physics engine provided by Gazebo offers an accurate prediction of the outcome in physical space. In future iterations, the model type and altitude will be varied to get a better sense of the environment, as inspired by another proof of concept implementing LiDAR for environment generation [35]. Figure 14: The entire image-to-simulation process: (**a**) Unaltered satellite image of urban intersection in Las Vegas, Nevada; (**b**) Las Vegas satellite image decorated with road mask; and (**c**) Las Vegas satellite image decorated with YOLO v4 results. Table 3 collects and compactly compares information about the aforementioned related works in relation to our platform. Table 4 provides a quantitative measurement of our framework's building placement accuracy. The Gazebo buildings were outlined and overlaid on an annotated satellite image, as described by Figure 17a-c. An additional example procedure is shown through Figure 18a-c. A pixel-by-pixel comparison of the real-life building locations to our simulated building placement was performed on a limited, randomized selection. The two painted images were overlaid, and the difference was recorded as \"Building Placement Error\". Figure 16: Unmanned Aerial Vehicles (UAV) interaction in Gazebo world: (**a**) scale view of drone in Las Vegas simulation relative to spawned elements; and (**b**) close-up of drone in Las Vegas simulation landing on building in Gazebo. Figure 15: Gazebo results: (**a**) resulting Las Vegas satellite image modeled in Gazebo (angled view); (**b**) resulting Las Vegas satellite image modeled in Gazebo (mid view); and (**c**) resulting Las Vegas satellite image modeled in Gazebo (top view). During our validation process, we noticed certain distinct building varieties resulting in a range of error, giving us insight on future improvements. While our platform is presently not fit with the complete models that make OpenUAV and AirSim advantageous, it adds the feature of simulating a real geographic location. This feature is crucial for a quality testbed, as users obtain a more complete illustration of how UAVs will perform upon deployment. Our simulation additionally allows for a variety of UAVs and automatic generation, which offers more scalability and ease of use than the Autonomous Airship and Vision-Based UAV Platforms. Finally, our platform removes the physical location restriction, making it much more practical than the Lincoln Lab Sim and the 2D UAS Collision Avoidance Sim. While there is still much to be added, our platform includes features necessary to create a quality UAV testing environment that other platforms lack. \begin{table}
\begin{tabular}{c c} \hline \hline
**Building Type Variety** & **Building Placement Error** \\ \hline Perpendicular, square & 9.7427\% \\ Irregular, asymmetric & 17.7916\% \\ Compound, jagged & 20.4725\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative validation framework. Validation summary for Gazebo model placement accuracy from a variety of satellite images. Figure 17: Validation process: (**a**) an original satellite image, unmarked for comparison in validation process; (**b**) the same satellite image, but, this time, real-life buildings are annotated and outlined; and (**c**) the same satellite image, but Gazebo-placed models are outlined instead.