Xie, X.; Qin, Y.; Zhang, Z.; Yan, Z.; Jin, H.; Xu, M.; Zhang, C. GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots. Sensors2024, 24, 1374.
Xie, X.; Qin, Y.; Zhang, Z.; Yan, Z.; Jin, H.; Xu, M.; Zhang, C. GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots. Sensors 2024, 24, 1374.
Xie, X.; Qin, Y.; Zhang, Z.; Yan, Z.; Jin, H.; Xu, M.; Zhang, C. GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots. Sensors2024, 24, 1374.
Xie, X.; Qin, Y.; Zhang, Z.; Yan, Z.; Jin, H.; Xu, M.; Zhang, C. GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots. Sensors 2024, 24, 1374.
Abstract
Simultaneous Localization and Mapping (SLAM), as one of the core technologies in intelligent robotics, has gained substantial attention in recent years. Addressing the limitations of SLAM systems in dynamic environments, this research proposes a system specifically designed for plant factory transportation environments, named GY-SLAM. GY-SLAM incorporates a lightweight target detection network GY based on YOLOv5, which utilizes GhostNet as the backbone network. This integration is further enhanced with CoordConv coordinate convolution, CARAFE up-sampling operators, and SE attention mechanism, leading to simultaneous improvements in detection accuracy and model complexity reduction. While improving mAP@0.5 by 0.514%, the model simultaneously reduces the number of parameters by 43.976%, computational cost by 46.488%, and model size by 41.752%. Additionally, the system constructs pure static octree maps and grid maps. Tests conducted on the TUM dataset and a proprietary dataset demonstrate that GY-SLAM significantly outperforms ORB-SLAM3 in dynamic scenarios in terms of system local-ization accuracy and robustness. It shows a remarkable 92.58% improvement in RMSE for Absolute Trajectory Error (ATE). Compared to YOLOv5s, the GY model brings a 41.5944% improvement in detection speed and a 17.7975% increase in SLAM operation speed to the system, indicating strong competitiveness and real-time capabilities. These results validate the effectiveness of GY-SLAM in dynamic environments and provide substantial support for the automation of logistics tasks by robots in specific contexts.
Copyright:
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