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Deep learning-enhanced smart ground robotic system for automated structural damage inspection and mapping
Automation in Construction ( IF 9.6 ) Pub Date : 2024-12-27 , DOI: 10.1016/j.autcon.2024.105951
Liangfu Ge, Ayan Sadhu

Ground robotic systems are essential for structural inspection, enhancing mobility, efficiency, and safety while minimizing risks in manual inspections. These systems automate 3D mapping and defect assessment in aging. However, current robotic platforms often require the integration of various sensors and complex parameter tuning, raising costs and limiting efficiency. This paper proposes a streamlined unmanned ground vehicle-based inspection platform, integrating only LiDAR and a low-cost monocular camera. Operated via the Robot Operating System, the platform deploys efficient instance segmentation, Simultaneous Localization and Mapping, and fusion algorithms, eliminating complex tuning across environments. A self-attention-enhanced YOLOv7 algorithm is proposed for accurate damage segmentation with limited datasets, while an enhanced KISS-ICP (Keep It Small and Simple-Iterative Closest Point) algorithm is developed to optimize point cloud odometry for efficient mapping and localization. By introducing camera-LiDAR information fusion, the proposed platform achieves structural mapping, damage localization, quantification, and 3D visualization. Laboratory and full-scale bridge tests demonstrated its high accuracy, efficiency, and robustness.

中文翻译:


深度学习增强型智能地面机器人系统,用于自动结构损伤检测和测绘



地面机器人系统对于结构检查至关重要,可以提高机动性、效率和安全性,同时最大限度地降低人工检查的风险。这些系统可自动进行老化的 3D 映射和缺陷评估。然而,当前的机器人平台通常需要集成各种传感器和复杂的参数调整,这增加了成本并限制了效率。该文提出了一种流线型的无人地面车辆巡检平台,仅集成LiDAR和低成本的单目相机。该平台通过机器人操作系统运行,可部署高效的实例分割、同步定位和地图构建以及融合算法,从而消除跨环境的复杂调整。提出了一种自我注意增强的 YOLOv7 算法,用于在有限的数据集下进行准确的损伤分割,同时开发了一种增强的 KISS-ICP (Keep It Small and Simple-Iterative Closest Point) 算法来优化点云里程计,以实现高效的映射和定位。通过引入摄像头-LiDAR 信息融合,所提出的平台实现了结构映射、损伤定位、量化和 3D 可视化。实验室和全尺寸桥梁测试证明了其高精度、高效率和稳健性。
更新日期:2024-12-27
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