# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Mapping Module  
To obtain more comprehensive 3D map information, we extended ORB-SLAM3 to achieve semantic map construction for lawn scenes. Originally, ORB-SLAM3 provided a sparse map, but we aimed to combine feature points and distance information from RGB-D cameras to generate a denser point cloud map. Building upon ORB-SLAM3, we introduced an additional thread dedicated to constructing a dense point cloud map. To avoid unnecessary calculations and redundant information, we chose to pass RGB images and depth information into the point cloud construction thread upon keyframe recognition, efficiently generating a dense point cloud map.  
Figure 6 illustrates how we improved the processing pipeline for point cloud maps. In the original dense point cloud construction process, the camera captures video and ORB-SLAM3 extracts keyframe information from the video. By combining RGB images and depth information from the keyframes, a dense point cloud is obtained. In our new processing pipeline, RGB images are inputted into our proposed MM-ED network model to obtain masked images. These masked images are then combined with depth information to obtain a 3D semantic map.  
Our point cloud construction system not only considers the geometric structure of the map but also incorporates semantic information by processing RGB images using the  
Figure 5: Visual SLAM system framework.  
Figure 6: Improvements in point-cloud-map-processing workflow.  
MM-ED neural network. This step aids in identifying regions in the image with semantic information, generating corresponding mask images. Subsequently, we construct a point cloud map based on this semantic information, enabling the map to not only include geometric structures but also possess semantic information. This enhances the map's expressive capabilities, significantly reduces point cloud storage space, and optimizes map representation efficiency by eliminating many irrelevant points.  
In this paper, we fully consider the semantic map construction module and propose the simultaneous construction of two forms of maps. By transforming point cloud information into an OctoMap [30] and an occupancy grid map [31], we comprehensively express the features and semantic information of the environment.  
OctoMap is a highly efficient three-dimensional spatial data structure. It implicitly represents and manages objects in space by recursively dividing three-dimensional space into eight subnodes. It does not require the explicit storage of triangular meshes, greatly saving memory. OctoMap can efficiently and finely manage and represent objects in three-dimensional space, which is a significant advantage of its use in three-dimensional applications. In terms of navigation, the octree structure efficiently represents hierarchical information in space, allowing the map to describe the details of the environment more finely, including objects and structures at different levels, providing good assistance for three-dimensional navigation.  
The advantage of occupancy grid maps lies in their ability to perform more accurate path planning. By partitioning a map into grids, obstacles and passable areas can be clearly represented, allowing for more precise calculations of optimal paths. Grid maps can also help a robot avoid obstacles and choose shorter, safer  
studies aimed at demonstrating the effectiveness of the improvements in the backbone and attention mechanism on model performance. We compare the proposed method with representative methods in the segmentation field, including U-Net [32], HRNet [33], FCN [34], and DeepLabv3+. In Section 4.5, we integrate the improved MM-ED network into the ORB-SLAM3 system for the establishment of a 3D point cloud semantic map, generating occupancy grid maps and OctoMaps. This step aims to validate the effectiveness of the mapping method proposed in this paper.