# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Visual SLAM System Framework  
In the exploration of unknown environments, mobile robots utilize various sensors to gather environmental information. Currently, there are two mainstream SLAM methods: Visual SLAM [25] and LiDAR-based SLAM [26]. They each have their advantages and find widespread application in their respective domains. LiDAR, which is used for constructing 3D point clouds, operates under various lighting conditions and provides precise measurements and localization. However, LiDAR devices are relatively expensive compared to the cameras used in Visual SLAM. Therefore, the lawn semantic map construction system designed in this paper utilizes Visual SLAM technology. The main visual sensors are categorized into three types, monocular cameras [27], stereo cameras [28], and RGB-D depth cameras, corresponding to different types of Visual SLAM technologies.  
Compared to monocular and stereo cameras, RGB-D cameras have significant advantages in building 3D point cloud maps. Firstly, RGB-D cameras can provide depth information for each pixel simultaneously while capturing color images, whereas stereo cameras require stereo-vision-matching calculations to obtain depth information. This allows RGB-D cameras to avoid additional and complex depth calculations during the mapping process, reducing the computational burden and enhancing real-time mapping efficiency. Secondly, RGB-D cameras can comprehensively capture the three-dimensional information of a scene. Monocular cameras provide only two-dimensional image information, and although stereo cameras can obtain depth information, their disparity calculations and matching process are susceptible to factors like lighting and texture. RGB-D cameras, by directly measuring depth, overcome these issues, resulting in a more accurate and stable construction of the 3D point cloud map. This is crucial for the navigation and localization of mobile robots in unknown environments. In summary, RGB-D cameras are superior at building 3D point cloud maps, providing essential support for the efficient and reliable perception and navigation of mobile robots in complex environments. Since the method proposed in this paper requires the construction of a 3D point cloud map, using an RGB-D camera is more efficient.  
SLAM technology mainly consists of four key components: sensor data acquisition, motion estimation and localization optimization, loop closure detection, and map construction. A typical Visual SLAM framework, as illustrated in Figure 5, is roughly divided into two main modules: a frontend and a backend. The frontend is responsible for sensor data collection and visual odometry, while the backend includes nonlinear optimization and map-building loop closure detection to correct the results obtained by the frontend. This system enables robots to explore unknown and unstructured environments, acquiring rich and specific information within their unstructured surroundings. This information can be utilized for future analysis, localization, and navigation within the environment.  
Figure 4: Deployment and invocation process of MM-ED model.  
The ORB-SLAM [29], proposed in 2015, stands out as one of the most sophisticated and user-friendly SLAM systems in contemporary robotics and computer vision applications. ORB-SLAM3, an enhanced version of its predecessor ORB-SLAM2, integrates state-of-the-art Oriented FAST and Rotated BRIEF (ORB) feature description and matching techniques. This system provides robust and efficient visual odometry and mapping capabilities. We will further explore the realm of 3D semantic maps of unstructured lawn scenes by combining our MM-ED model with ORB-SLAM3.