We report system and algorithmic development for a sensing suite comprising multiple sensors for both surface and subsurface transportation infrastructure inspection focusing on multi-modal mapping for inspection. The sensing suite contains a camera, a ground penetrating radar (GPR), and a wheel encoder. We design the sensing suite and propose a data collection scheme using customized artificial landmarks (ALs). We use ALs to synchronize two types data streams: camera 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. The sensor fusion approach reduces the end-to-end distance error from 7.45cm to 3.10cm.
展开▼
