# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 5 Conclusions and Future Work  
This paper addresses the construction of a 3D semantic map for a lawn-mower robot operating in an unstructured lawn environment. The proposed multi-object semantic segmentation detection network MM-ED is integrated with the ORB-SLAM3 system to generate a 3D semantic map, facilitating a robust three-dimensional reconstruction of the working area and providing rich environmental information for future robotic weeding operations. To validate the effectiveness of the MM-ED network proposed in this paper, a lawn dataset was created, and comparative experiments with other models were conducted. The main conclusions of this study are as follows: (1) The MM-ED network proposed in this paper utilizes the MobileNetV2 backbone and incorporates the ECA attention mechanism to enhance feature recognition in images. The experimental results demonstrate that compared to other semantic segmentation models with similar MIoU accuracy values, the MM-ED network reduces the model's parameter count and improves its recognition speed. (2) The experimental results indicate that the proposed method performs well in recognizing major objects on a lawn, achieving the satisfactory identification of various  
Figure 14: Generating a wide variety of maps.  
items. (3) The method proposed in this paper is easy to deploy, allowing for convenient integration with the ORB-SLAM3 system. In the future, it can be easily migrated to embedded devices, and it effectively generates 3D semantic maps, octree maps, and occupancy grid maps for unstructured lawn environments.  
Although the 3D semantic map construction method for unstructured lawn scenes proposed in this paper currently demonstrates effective mapping of the working area, there are certain limitations. The current MM-ED network can recognize humans, but in the context of lawn-mower-robot operation, humans should be treated as dynamic targets and removed to prevent interference with map creation. Additionally, this study only established a semantic point cloud map and obtained octree and occupancy grid maps. Future research could focus on designing working paths for lawn-mower robots based on the acquired maps. Therefore, in future work, we will continue to address these issues and explore innovative solutions.  
Resources, formal analysis, conceptualization, methodology, and funding acquisition: X.X.; validation and writing--original draft preparation: Z.Y.; investigation and methodology: Z.Z.; data curation: Y.Q.; software: H.J.; visualization: M.X.; software: C.Z. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Longmen Laboratory \"Trendy Industry Projects\" (No. LMFKCY2023002), the Luoyang Municipal Public Welfare Special Project (No. 2302031A), and the Science and Technology Tackling Key Problems Project of Henan Province (No. 232102110294).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.