3D reconstruction of road surfaces using an integrated multi-sensory approach

In this paper, we present our experience in building a mobile imaging system that incorporates multi-modality sensors for road surface mapping and inspection applications. Our proposed system leverages 3D laser-range sensors, video cameras, global positioning systems (GPS) and inertial measurement units (IMU) towards the generation of photo-realistic, geometrically accurate, geo-referenced 3D models of road surfaces. Based on our summary of the state-of-the-art systems for a road distress survey, we identify several challenges in the real-time deployment, integration and visualization of the multi-sensor data. Then, we present our data acquisition and processing algorithms as a novel two-stage automation procedure that can meet the accuracy requirements with real-time performance. We provide algorithms for 3D surface reconstruction to process the raw data and deliver detail preserving 3D models that possess accurate depth information for characterization and visualization of cracks as a significant improvement over contemporary commercial video-based vision systems.