An Intelligent Robotic Sensing System for Indoor Building System Inspection

Regular indoor building inspections and maintenance are essential to prevent property damage and sustain its condition and functionality for the intended occupants. Traditional inspection is heavily labor-intensive, time-consuming, experience-based, and error-prone. With the growing development of multi-sensor and artificial intelligence (AI)-empowered unmanned ground vehicles (UGV), inspection activities can be automated and digitalized. However, there is a lack of knowledge on how to incorporate them effectively for streamlined inspection of indoor building systems. This paper proposes a workflow of integrating robots, multimodal imagery sensors (e. g., infrared camera, LiDAR, RGB camera), and AI-based data analytics to support real-time detection and assessment of defects within indoor building environments. In addition, the detected imagery anomalies will be geo-registered and located into a 3D building model, to support further analysis and evaluation of building conditions. The successful implementation of the envisioned intelligent robotic sensing system can lead to a significantly more efficient and responsive diagnosis of indoor building systems.