The increase in the development of digital twins brings several advantages to inspection and maintenance, but also new challenges. Digital models capable of representing real equipment for full remote inspection demand the synchronization, integration, and fusion of several sensors and methodologies such as stereo vision, monocular Simultaneous Localization and Mapping (SLAM), laser and RGB-D camera readings, texture analysis, filters, thermal, and multi-spectral images. This multidimensional information makes it possible to have a full understanding of given equipment, enabling remote diagnosis. To solve this problem, the present work uses an edge-fog-cloud architecture running over a publisher-subscriber communication framework to optimize the computational costs and throughput. In this approach, each process is embedded in an edge node responsible for prepossessing a given amount of data that optimizes the trade-off of processing capabilities and throughput delays. All information is integrated with different levels of fog nodes and a cloud server to maximize performance. To demonstrate this proposal, a real-time 3D reconstruction problem using moving cameras is shown. In this scenario, a stereo and RDB-D cameras run over edge nodes, filtering, and prepossessing the initial data. Furthermore, the point cloud and image registration, odometry, and filtering run over fog clusters. A cloud server is responsible for texturing and processing the final results. This approach enables us to optimize the time lag between data acquisition and operator visualization, and it is easily scalable if new sensors and algorithms must be added. The experimental results will demonstrate precision by comparing the results with ground-truth data, scalability by adding further readings and performance.

中文翻译：

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支持数字双胞胎以进行远程检查的异构边缘雾环境。

数字双胞胎的发展增长为检查和维护带来了许多优势，同时也带来了新的挑战。能够代表真实设备进行全面远程检查的数字模型需要多种传感器和方法的同步，集成和融合，例如立体视觉，单眼同时定位和制图（SLAM），激光和RGB-D摄像机读数，纹理分析，滤镜，热和多光谱图像。这种多维信息可以全面了解给定的设备，从而实现远程诊断。为了解决这个问题，本工作使用运行在发布者-订户通信框架上的边缘雾云架构来优化计算成本和吞吐量。用这种方法 每个过程都嵌入到一个边缘节点中，该边缘节点负责处理给定数量的数据，从而优化处理能力和吞吐量延迟之间的权衡。所有信息都与不同级别的雾节点和云服务器集成在一起，以最大化性能。为了演示该建议，显示了使用移动摄像机的实时3D重建问题。在这种情况下，立体声和RDB-D摄像机在边缘节点上运行，进行过滤并预先放置初始数据。此外，点云和图像配准，测距和滤波在雾簇上运行。云服务器负责纹理化和处理最终结果。这种方法使我们能够优化数据采集和操作员可视化之间的时间间隔，并且如果必须添加新的传感器和算法，则可以轻松扩展。