Towards Automated Post-Disaster Damage Assessment of Critical Infrastructure with Small Unmanned Aircraft Systems

Maintaining secure and resilient critical infrastructure including assets, systems, and networks such as roads, pipelines, bridges and railroads is a matter of homeland security. This paper proposes an efficient and fast infrastructure inspection method by using autonomous small Unmanned Aircraft System (sUAS) to document damage after extreme events such as hurricanes, tornadoes, or earthquakes. Our research team has been selected to participate in the 2017 National Infrastructure Protection Plan (NIPP) Security and Resilience Challenge organized by the Department of Homeland Securitys Office of Infrastructure Protection and the National Institute for Hometown Security. We propose to use a sUAS equipped with visual sensors, such as Lidar and cameras, as a post-disaster data collection, detection and assessment system. Point cloud data collected is used for 3D structure modeling, forming the basis of the automated damage assessment. Existing damage in structures are then detected and quantified using proposed damage detection methods, including surface normal based method for detecting damage with small deformation and graph-based method for detecting damage that occur with large deformation. This method provides new capabilities for automated damage analysis with sUAS as a key enabling technology for documenting damage in critical infrastructure to facilitate the post-disaster inspection and recovery of critical infrastructures.