Roofus: Learning-based Robotic Moisture Mapping on Flat Rooftops with Ground Penetrating Radar

Abstract

Robust moisture detection is crucial for building maintenance and cost reduction. Current methods are often limited by the type of roofing material or are cumbersome and expensive. Ground Penetrating Radar (GPR) has shown promise in recent works in moisture detection due to its effectiveness across a broader range of materials, its compactness and lightweight nature, and its ability to image the subsurface. We introduce Roofus, an integrated robotic moisture detection system for flat rooftops, designed to overcome traditional method limitations. It combines a remotely controlled robot with deep learning GPR data processing and automatic map generation. Real-world data is collected and manually annotated for supervised learning. We investigate a novel approach to interpreting GPR data via deep learning using Transformer-based classifiers. LiDAR inertial odometry is employed to integrate multiple individual GPR scans into a holistic moisture map over the rooftop. When evaluated against existing methods such as infrared thermal imaging, electrical capacitance surveys, and nuclear moisture gauges, our system shows promising viability for industry application.