Multi-camera vision-based synchronous positioning and mapping for green construction of electric substations - Wang, Chang; Yuan, Huihong; Bian, Qifeng; Zhao, Chongjuan; Zhou, Botao; Chen, Zhuozhou; Basit, Muhammad A. (Frontiers in Energy Research, 2024)
To address climate change and promote a low-carbon economy, precise CO 2 emission estimation is essential in electric substation construction. Camera-based monocular SLAM methods are employed in electric substation construction for dynamic monitoring, providing spatial data for precise CO 2 emission tracking. But traditional monocular SLAM methods face significant challenges, including sunlight interference, terrain complexity, sensor failures, and a lack of stable textures. 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 CO 2 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 CO 2 estimation, enhancing decision-making, resource management, and sustainable energy development.