Abstract
Nowadays, numerous facilities that provide essential services operate with little or even no human supervision. In this context, electrical power substations can be entirely autonomous or remotely operated, and may be difficult to access. This scenario increases the need of remote monitoring for predictive maintenance and problem analysis by using imaging and 3D reconstruction of the environment. 3D reconstruction processes are concentrated in academic and commercial applications, but they are non scalable and fail to provide remote real time data analysis. This research proposes a novel color 3D scanner architecture environment for remote real time multiple sensor processing and reconstruction. The goal is to present a more efficient system based upon scalability and low latency applications, where multiple sensors can be added without losing the overall processing capability. For this purpose, this study approaches this problem in two ways. First, it improves 3D reconstruction algorithms by enhancing individual performance. Second, to optimize the entire system, it distributes the running processes in individual layers, interconnected by an edge–fog architecture. This architecture enables the use of multiple devices by optimizing payload distribution, latency, and throughput in the network. Unlike previous studies, the results present a thorough analysis of architecture efficiency when multiple sensors are operating in parallel instead of the traditional centralized architecture. Finally, this work provides the basis for real-time remote presence applications.
•A new approach of distributed sensor fusion.•An Edge–Fog Architecture for 3D reconstruction.•Very Fast and Reliable.•Tested in real cases.•Applied in electrical power stations remote supervision.
