# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 5 Experimental Results
### Real-World Outdoor Experiments
#### 5.2.2 Place Recognition  
To verify the accuracy of the network architecture used in this study, the performance of several well-known models was compared using \(D_{Scene}\) and \(D_{Land}\). These models were  
Figure 11: Based on the image and point cloud information acquired during the experiment and the confirmation of the landing site, (**i**–**iii**) illustrate the steps of the experimental process, and (**a**–**e**) show the types of experimental sites selected for the outdoor tests (part of the point cloud map is rotated). The blue points marked in the lower column show the approximate landing site selection, and the red points show the final landing site. deployed on NVIDIA NX to test the efficiency of the algorithm on an edge computing platform. The models were trained and tested with a validation set of 81,970 images (same images but different classifications), and the results are summarized in Table 3. The performances of various models were evaluated using the validation dataset, thus highlighting their applicability and effectiveness in real-world scenarios. Analyzing the accuracy of the validation data (right-most column of Table 3) revealed that the ResNet series of models demonstrated outstanding performance compared with other basic network architecture frameworks. In particular, ResNet50 and ResNet101 excelled in terms of accuracy. When the dataset was subdivided into seven main categories, as shown in Table 3, each network's performance on the \(D_{Land}\) dataset exhibited stronger generalizability. Almost all networks showed significant improvements in recognition accuracy during inference tasks. This confirmed that \(D_{Land}\), which directly determined whether the scene was suitable for landing, was a better scheme than \(D_{Scene}\), which determined the properties of the scene before deciding whether the scene was suitable for landing. In addition, this method verified that reducing the dataset in this way could improve the practicability of the UAV's ability to perform complex scene recognition tasks. In contrast to approaches like that described by [PERSON] et al. [24], which start with semantic segmentation before identifying safe landing zones [18] with the same kind of dataset, this work leverages a point cloud costmap to obviate the segmentation process when selecting landing areas. By relying on a CNN for semantic scene recognition, this approach streamlines the algorithm and significantly enhances its stability. A performance evaluation was carried out on the test dataset comprising 5000 images of multiple scenes collected by aerial photography to ensure the efficient operation and stability of airborne computing systems in practical environments and to effectively reduce potential overload risks. Performance metrics, such as storage requirements (i.e., ROM), runtime, and memory usage (i.e., RAM) were examined for image classification tasks in airborne computers. Table 4 shows the performance of various network models for these computational performance metrics. ResNeXt50 and ResNet50 showed excellent resource utilization. In contrast to the deeper ResNet101 or the shallower ResNet18 and ResNet34, ResNet50 provided a better balance, maintaining higher accuracy while requiring fewer computational resources. This justified the selection of ResNet50 as the primary model for the UAV landing area recognition algorithm proposed herein.