# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 5 Experimental Results
### Simulation Experiments  
In the simulation, the Robot Operating System (ROS) served as middleware to facilitate real-time execution of various processes, including state estimation, trajectory planning, landing site detection, LIDAR, and image processing. As described in Section 1, the UAV was equipped with a LIDAR system for point cloud collection, a binocular camera for image acquisition, and an IMU for UAV motion data feedback. The sensor setup was based on its true parameters, as shown in Figure 8a. Figure 8b-e show the four simulation environments constructed for this study. To more accurately reflect real-world conditions in the simulation, a disturbance factor was introduced, we set the local average wind speed to 12 km/h, and we did not use tools outside the UAV (e.g., RTK). An iconic crossroad scene was selected for a detailed analysis. Figure 7: Schematic diagram of the UAV experimental platform. In terms of trajectory selection, a circuitous path was adopted and evaluated within the simulation environment, as depicted in Figure 9a. Within the chosen trajectory (Table 1), the density of point clouds per square meter (\(\rho_{map}\)) over the total scanning time (\(T_{total}\)) was consistent with the UAV equipped with LIDAR to perform high-precision scene modeling. It successfully completed the task, maintaining a levelness deviation (\(\triangle\alpha_{scene}\)) \(\leq\) 1.5 \({}^{\circ}\) at 10 randomly selected locations across each scene. Figure 9b shows the UAV's flight trajectory to the designated landing sites. In terms of UAV control, the Proportional-Integral-Derivative (PID) method [50] used for trajectory tracking managed the UAV's motor speed to follow the planned trajectory. It adjusted the motors based on the current position error, past errors, and the predicted future errors. This simple yet effective approach allowed the UAV to accurately maintain \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(T_{total}\) & \(\rho_{map}\) & \(\triangle\alpha_{scene1}\) & \(\triangle\alpha_{scene2}\) & \(\triangle\alpha_{scene3}\) & \(\triangle\alpha_{scene4}\) \\ (s) & **(points/m2)** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** \\ \hline
36.11 & \(111\) & \(\pm\)1.19 & \(\pm\)0.89 & \(\pm\)0.96 & \(\pm\)1.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of point cloud densities collected by a UAV in the selected trajectory and the levelness deviation among four scenes. Figure 8: Simulation of UAVs and constructed scenes. (**a**) System configuration of the UAV in the simulation including Livox Horizon and RealSense with scanning ranges parallel to the ground; the images in (**b–e**) are virtual environments based on four common scenarios. Figure 9: Running trajectories of the UAV: (**a**) terrain scanning trajectory of the UAV; (**b**) landing designated trajectory of the UAV. its course and respond dynamically to any environmental changes, thus ensuring stable flight and precise trajectory tracking. During the simulation phase, a comparative analysis was conducted for the UAV positioning methods: GPS, Optical Flow [16], Hybrid Optical Flow [17], and this work. Due to the limitations of image recognition in the simulation environment, a specific location from the initial site selection was used for landing. Figure 10a shows the UAV's trajectory tracked using different methods. Figure 10b-e present the average absolute errors in the \(x\), \(y\), \(z\), and \(yaw\) dimensions relative to the expected values for each method in the simulation. Analyzing positional errors revealed that Optical Flow [16] had notable deviations during long-distance tracking. The position adjustments using GPS and Hybrid Optical Flow [17] showed more fluctuations. In contrast, the method developed in this study leveraged the perception of environmental distances, resulting in superior positioning accuracy.