# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 6 Conclusions  
This report described a comprehensive framework for UAV landing in various environments. To solve the localization problem facing UAVs in GPS-denied environments, a LIDAR-camera system was developed to generate geometric and color terrain maps of the environment. Then, based on the LIDAR-camera mapping technology, a cost function combining the weighted energy texture and geometric plane was proposed to identify and evaluate potential landing sites. The cost function combined 3D terrain and 2D scene recognition processes, which increased the rationality and safety of UAV landing site selection. Finally, a dataset classification method suitable for UAV landings based on scene landing difficulty was proposed, and a CBAM+ResNet50 network structure was designed for dataset classification to ensure a safe final landing. Extensive experiments were carried out in simulated and real-world scenarios. In the simulated scenarios, the selection of a UAV operating trajectory was confirmed, and the developed approach was compared with various positioning methods to verify the stability of the proposed positioning scheme in long-distance trajectories by measuring the error relative to the ideal trajectory. In the real scenarios, a variety of network structures were compared with the classification method proposed in this study using a traditional dataset that judged the landing rationality based on certain attributes; all were deployed to the real machine for testing. The results showed that the proposed dataset and network structure improved UAVs' scene classification and were suitable for UAV scene recognition for landing. In addition, the entire system was tested extensively in different types of scenarios to verify the robustness of the overall LIDAR-camera method. Future research efforts will aim to enhance the landing trajectory selection process, UAV motion control algorithm, and network structure utilization.  
Conceptualization, C.Z. and L.K.; methodology, C.Z.; software, C.Z.; validation, C.Z. and Y.S.; formal analysis, C.Z. and Y.S.; investigation, C.Z. and Y.S.; resources, C.Z. and L.K.; data curation, C.Z. and Y.S.; writing--original draft preparation, C.Z. and Y.S.; writing--review and editing, C.Z. and Y.S.; visualization, C.Z. and Y.S.; supervision, L.K.; project administration, C.Z. and L.K.; funding acquisition, C.Z. and L.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fujian Natural Science Foundation Project (project no. 2021J05213) and Scientific Research Start Foundation of Fujian University of Technology (project no. GY-Z20167).  
Not applicable.  
The data and code that support the findings of this study are available from the corresponding authors upon reasonable request.  
We would like to express our sincere gratitude to all editors, reviewers, and staff who participated in the review of this article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithm** & **Time Cost (ms)** \\ \hline Down simple & \(101.75\pm 5.49\) \\ Flatness & \(103.35\pm 8.27\) \\ Smooth canny & \(8.64\pm 2.51\) \\ Euclidean distance & \(7.17\pm 2.93\) \\ Places classification & \(3920.21\pm 54.04\) \\ Total & \(4141.12\pm 73.24\) \\ \hline \end{tabular}
\end{table}
Table 7: The computational runtime of the proposed landing site identification method.