# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Lawn Map Construction Experiment  
This section validates the method proposed in this paper for constructing a 3D semantic map of a lawn scene through experimentation. The experiment utilized an Intel Realsense D455 depth camera with a resolution of 640 \(\times\) 480. The program ran on a laptop running Ubuntu 20.04. The depth camera was handheld and walked around the lawn that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MIoU/\%** & **MPA/\%** & **GFLOPs** & **Params** & **FPS** \\ \hline DeepLabv3+ & 92.28 & 95.52 & 176.52 & 39.31 & 14.86 \\ Ours & 91.07 & 94.71 & 68.25 & 15.10 & 27.69 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Our architecture achieves similar MIoU and MPA values as the computationally expensive DeepLabv3+.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MIoU/\%** & **MPA/\%** & **GFLOPs** & **Params** & **FPS** \\ \hline FCN & 89.71 & 94.66 & 197.86 & 44.94 & 16.25 \\ HRNet18 & 90.04 & 94.28 & 18.60 & 9.19 & 20.94 \\ U-Net & 90.57 & 94.69 & 203.04 & 27.64 & 21.21 \\ Ours & 91.07 & 94.71 & 68.25 & 15.10 & 27.69 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with other networks: our network significantly outperforms others in terms of MIoU and MPA.  
Figure 12: Experimental results and comparison with other methods.  
needed mapping. During the walking process, the camera was continuously swayed from side to side to cover the entire lawn that needed to be captured. Figure 13 illustrates the real-time recognition of the lawn during the walking process.  
It can be seen in the process of building a 3D semantic map that the architecture proposed in this paper demonstrates relatively good recognition of the working lawn. In general, it can accurately identify all six categories that need to be identified. According to the method mentioned in Section 3.2, the results of the semantic segmentation are added to the ORB-SLAM3 system. Through this system, a 3D semantic point cloud map can be obtained. As shown in Table 8, compared with the original RGB point cloud map, the semantic point cloud map occupies much less space. Since the semantic map constructed in this paper is for the establishment of a working space service for gardening pruning robots, only a semantic map based on the working height of the robot is needed. The crowns at the tops of trees have no effect on the lawn moving task of the robot, so they are all removed. This makes the saved point cloud map more abundant, and more effective information is saved in the same point cloud map.  
As relying solely on a point cloud map is insufficient for path navigation, the 3D semantic point cloud map needs to be converted into an octree map and an occupancy grid map suitable for future three-dimensional and two-dimensional path navigation. Figure 14 illustrates the colored point cloud map, semantic point cloud map, octree map, and occupancy grid map obtained using the MM-ED method proposed in this paper. As the presence of moving individuals in the environment can significantly impact the 3D semantic point cloud map, the point cloud maps established in this experiment exclusively feature scenes without any people.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Point Cloud File Type** & **Size of Space/MB** \\ \hline RGB Point Cloud & 68.5 \\ Semantic Point Cloud & 42.9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Point cloud map size comparison results.  
Figure 13: Semantic segmentation of RGB images using DeepLabv3+ in ORB-SLAM3 build maps.