Encoder-Camera-Ground Penetrating Radar Tri-Sensor Mapping for Surface and Subsurface Transportation Infrastructure Inspection

We report system and algorithmic development for a sensing suite comprising multiple sensors for both surface and subsurface transportation infrastructure inspection where we focus on multi-modal mapping for inspection. The sensing suite contains both inspection sensors, such as a camera and a ground penetrating radar (GPR), and navigational sensors, such as a wheel encoder or a global positioning system receiver. We design and build the sensing suite and propose a data collection scheme using the customized artificial landmarks (ALs). We use ALs to synchronize two types data streams: cameras images that are temporally evenly-spaced and GPR/encoder data that are spatially evenly-spaced. We also employ pose graph optimization with synchronization as penalty functions to further refine synchronization and perform data fusion for 3D reconstruction. We have implemented the system and tested it in physical experiments. The results show that our system successfully fuses three sensory data and product metric 3D reconstruction for them. Moreover, the sensor fusion approach reduces the end-to-end distance error from 7.45cm to 3.10cm.