| Project Detail |
Carnegie Mellon University (CMU) will develop a general-purpose mapping system that can integrate with virtually any mobile robot dedicated to pipe inspection and repairs. Confined spaces challenge map creation because they limit payload size. This not only affects the choice of sensor, but how its information is processed because of the space required to store edge computing. On top of that, confined spaces challenge the use of the sensors themselves; most sensors have a lower limit on sensing range, which is often violated in small spaces. Finally, pipe-confined spaces lack features, making mapping quite difficult. The team’s approach (1) fuses data from multiple sensory sources to accurately and reliably map the structure of a large-scale, distributed pipe structure while gathering information on its health, (2) creates a novel sensor payload specifically designed to operate in confined spaces to provide visual and geometric inspection, and (3) takes in and manipulates large-scale point cloud data in computationally efficient ways to improve visualization and the opportunity for modern techniques in AI to provide post-processing support.
Potential Impact:
REPAIR seeks to eliminate the highest pipe rehabilitation costs, excavation and restoration, by repairing pipes without their removal. |
