Multi-camera vision-based synchronous positioning and mapping for green construction of electric substations

Abstract

Accurate CO2 tracking in electric substation construction is vital for climate efforts, using monocular SLAM for monitoring despite challenges like sunlight and complex terrain. Additionally, these methods typically yield only aggregate carbon emission data, thereby lacking the granularity necessary for precise monitoring throughout the construction process. These limitations compromise mapping accuracy and impede the integration of digital twins and IoT technologies. Addressing these issues, this paper proposed a methodology combining red, green, and blue (RGB) cameras and multi-camera collaboration with digital design systems, enhancing SLAM capabilities. The advanced technique integrated methods including overlap estimation, depth reasoning, noise reduction, and surface reconstruction to create accurate 3D models, enhancing scene reconstruction and real-time CO2 tracking during construction and operation. Leveraging continuous on-site camera monitoring as a substitute for manual inspections, it significantly contributes to the compilation of a comprehensive carbon emission database within a digital twin framework. Experimental results confirmed the proposed method’s superiority over previous works in real-time CO2 estimation, enhancing decision-making, resource management, and sustainable energy development. Overall, besides its application in substation construction for CO2 monitoring, this methodology can also be applied to carbon tracking in various other construction projects.