A BIM-enabled digital twin framework for real-time indoor environment monitoring and visualization by integrating autonomous robotics, LiDAR-based 3D mobile mapping, IoT sensing, and indoor positioning technologies

Abstract

Existing indoor environment monitoring methods often fall short due to the lack of using standardized evaluation methods, missing spatial information, or the absence of user-friendly visualization interfaces. To address these limitations, this paper proposes a novel digital twin framework for real-time indoor environment monitoring and visualization by integrating Building Information Modeling (BIM), Internet of Things, autonomous robot-based mobile mapping and sensing, and indoor positioning technologies. The goal is to autonomously monitor indoor environmental conditions and provide real-time visualization using a standardized assessment index with estimated spatial information in BIM software. The proposed framework was demonstrated and validated in two indoor scenarios for air quality monitoring and visualization-related applications. Implementation results revealed that: the framework effectively captured spatial characteristics of air quality in small and medium-sized indoor spaces and intuitively visualized it using a BIM digital twin model; and, the ultra-wideband-based indoor positioning system achieved a nearly centimeter-level positioning precision. Practical interpretations of the results demonstrated that the proposed framework can not only detect and localize the potential sources of air pollutants but can also identify the possible causes of low indoor air quality. This study ultimately contributes to the body of knowledge by offering a novel and generic digital twin framework that seamlessly bridges various popular yet previously isolated technologies for better informing building facility management-related decisions. This paper is the first research work that integrates several emerging technologies (i.e., BIM, digital twins, IoT sensing, indoor localization/positioning/tracking, and robotics) for advancing real-time monitoring and visualization of indoor environmental conditions. While the proposed framework was applied for indoor air quality monitoring, it could be extended and used for various indoor environmental assessment and visualization-related applications.