# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
###### Abstract  
Traditional automatic gardening pruning robots generally employ electronic fences for the delineation of working boundaries. In order to quickly determine the working area of a robot, we combined an improved DeepLabv3+ semantic segmentation model with a simultaneous localization and mapping (SLAM) system to construct a three-dimensional (3D) semantic map. To reduce the computational cost of its future deployment in resource-constrained mobile robots, we replaced the backbone network of DeepLabv3+, ResNet50, with MobileNetV2 to decrease the number of network parameters and improve recognition speed. In addition, we introduced an efficient channel attention network attention mechanism to enhance the accuracy of the neural network, forming an improved Multiclass MobileNetV2 ECA DeepLabv3+ (MM-ED) network model. Through the integration of this model with the SLAM system, the entire framework was able to generate a 3D semantic point cloud map of a lawn working area and convert it into octree and occupancy grid maps, providing technical support for future autonomous robot operation and navigation. We created a lawn dataset containing 7500 images, using our own annotated images as ground truth. This dataset was employed for experimental purposes. Experimental results showed that the proposed MM-ED network model achieved 91.07% and 94.71% for MIoU and MPA metrics, respectively. Using a GTX 3060 Laptop GPU, the frames per second rate reached 27.69, demonstrating superior recognition performance compared to similar semantic segmentation architectures and better adaptation to SLAM systems. s +
Footnote †: journal: Applied sciences  
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Footnote 1: Longmen Laboratory, Luoyang 471003, China; [EMAIL_ADDRESS]  
2021041670 as a boundary detection problem [7]. When addressing the issue of lawn area and its boundary detection, it is necessary to identify the lawn and other elements by analyzing the semantics of the image scene. Based on this information, the boundaries of the lawn and the positions of obstacles must be located to support the robot's autonomous navigation and obstacle avoidance. Typically, the maintenance of lush lawns is carried out by gardeners using hand-held mowers or riding mowers. In recent years, some smart lawn mowers have also emerged, but they often require the installation of electronic fences [8] before they begin working. These fences delineate the robot's maximum working range to prevent it from entering inappropriate areas and causing equipment damage. Overall, traditional lawn-mowing methods primarily rely on the autonomous responses of humans or robots, lacking a holistic awareness of the required working area. Lawn trimming is usually a manual, labor-intensive, and time-consuming task. However, with labor shortages, aging populations, and declining birth rates becoming common challenges faced by agriculture and other labor-intensive industries, the development of robotic technology becomes an optimal strategy for addressing this problem, enabling robots to replace gardeners in performing lawn trimming tasks. In recent years, semantic image segmentation [9] has emerged as a key task in image processing. This rapid progress has led to a paradigm shift in the way many fields approach problem solving. Consequently, the benefits of utilizing semantic image segmentation in autonomous lawnmowers are evident. It enables better recognition of the surrounding environment, accurately identifying environmental information, thereby facilitating the creation of a global map of the environment for improved path planning in the future. In this study, we created a lawn dataset using self-annotated images as ground truth. We employed the proposed Multi-Class MobileNetV2 ECA DeepLabv3+ (MM-ED) network to segment the lawn dataset and compared its performance with other deep learning algorithms.