# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 5 Experimental Results
### Real-World Outdoor Experiments
#### 5.2.3 Landing Analysis  
The evaluation index for landing stability in the final moment of landing \(t_{L}\) relied on the average horizontal degree \(\alpha_{L}\) of the selected site, the offset \(d_{L}\) from the expected position, the vertical-horizontal direction speed \(v_{z}\), and the vertical-horizontal direction acceleration \(a_{z}\). The UAV position posture at \(t_{L}\) was also considered, as shown in Table 5. The reference object was detected using the methods evaluated in this study, i.e., GPS, Optical Flow [16], Hybrid Optical Flow [17], and our method, and the UAV landed at the designated position. The latitude was set as the \(y\)-axis, and the longitude was set as the \(x\)-axis. A coordinate system was established to assess how the different landing solutions deviated from the target point during the actual landing. Figure 12 shows the deviation from the expected target point of the UAV landing area when five experiments were conducted using each of the four methods. The GPS landing approach had larger errors due to the noise introduced \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Network** & **ROM (Mb)** & **RAM (Mb)** & **RunTime (s)** & **Loss** & **F1** & **Recall** &
\begin{tabular}{c} **TOP1** \\ **Accuracy** \\ \end{tabular} \\ \hline ResNeXt50 & 88 & 901.493 & 0.95 & 0.342 & 0.726 & 0.712 & 0.794 \\ Vgg19 & 532 & 896.237 & **0.84** & 0.331 & 0.713 & 0.703 & 0.761 \\ GoogleNetV4 & 157 & 887.847 & 1.09 & 0.385 & 0.748 & 0.735 & 0.792 \\ MobileNetV3 & 154 & 914.367 & 1.18 & 0.379 & 0.734 & 0.721 & 0.777 \\ ResNet18 & **72** & 966.265 & 0.92 & 0.396 & 0.682 & 0.657 & 0.761 \\ ResNet34 & 83 & 923.461 & 0.96 & 0.329 & 0.701 & 0.673 & 0.773 \\ ResNet50 & 90 & 874.207 & 0.96 & 0.336 & 0.716 & 0.698 & 0.799 \\ ResNet101 & 162 & 863.052 & 1.03 & **0.314** & 0.765 & 0.753 & 0.817 \\ ResNet50+CBAM & 92 & **855.925** & 0.98 & 0.328 & **0.817** & **0.823** & **0.841** \\ \hline \hline \end{tabular}  
* The bold values indicate the best performance among the compared methods.  
\end{table} 
Table 4: Performance results of different network architectures on the test dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\alpha_{L}\) (\({}^{\circ}\)) & \(d_{L}\) (m) & \(v_{z}\) (m/s) & \(a_{z}\) (m/s\({}^{2}\)) \\ \hline GPS & 3.11 & 1.41 & 0.32 & 0.21 \\ Optical Flow [16] & 2.36 & 1.31 & 0.29 & 0.18 \\ Hybrid Optical & **1.83** & 0.54 & 0.17 & 0.15 \\ Flow [17] & 1.99 & **0.41** & **0.10** & **0.04** \\ \hline \hline \end{tabular}  
* The bold values indicate the best performance among the compared methods.  
\end{table} 
Table 5: Evaluation of site selection results obtained by different methods. by the device itself. Over multiple experiments, the Hybrid Optical Flow significantly improved its performance [17]. The method developed in this study could achieve high landing accuracy and meet the needs of emergency situations as an alternative to traditional methods (green arrow in Figure 12) because it used a map generated from collected point clouds for more accurate positioning. This resulted in closer proximity to the landing center point. The algorithm leveraged the precision of LIDAR to identify the distance to obstacles during landing, which significantly enhanced the accuracy and robustness of detecting the UAV's altitude relative to the ground. This advantage is detailed in Table 5, which illustrates the effectiveness of this approach compared with other methods. As a result, the UAV could maintain a safe flight speed during landing, thus enabling it to smoothly land at the target point. The real-world UAV landing performance of the proposed method, demonstrated across five types of scenarios, is presented in Table 6. As expected, with the algorithm, the UAV autonomously landed in safe and relatively flat areas without the need for GPS, thus highlighting its effectiveness, especially in emergency situations.