NRI: Collaborative Research: Autonomous Quadrotors for 3D Modeling and Inspection of Outdoor Infrastructure

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This project develops technologies to collect visual and inertial data necessary for constructing, offline, high-accuracy 3D maps of the structure for civil and industrial infrastructure such as bridges, power plants, and refineries. It also develops technologies for online processing including localization, path planning and obstacle avoidance. The project builds a system that employs quadrotors to assist their human co-workers in visual inspections of the outdoor infrastructure to enhance efficiency and effectiveness of such operations. The research advances the current state of the art in key areas of sensing, estimation, and control necessary for enabling small-size quadrotors to assist humans in visual inspections. In addition to improving the reliability of the nation's infrastructure, the project benefits researchers, developers, educators, and end-users in robotics by developing open-source, modular algorithms for quadrotors. The project offers educational and community outreach activities aligned with local efforts and state-wide initiatives, and seeks to increase diversity and attract underrepresented groups to Science, Technology, Engineering, and Mathematics (STEM) via a partnership with local high schools. This research addresses the fundamental challenges stemming from sensing and processing limitations that prevent the use of low-cost, small-size quadrotors in visual-inspection tasks. It focuses on a four-step process, where initially a quadrotor is tele-operated at a safe distance from the structure of interest to collect visual and inertial data necessary for constructing, offline, high-accuracy 3D maps of the structure. These maps are then used, by the inspection engineer, to designate areas of interest. Lastly, the quadrotor employs its onboard sensors to precisely localize with respect to the structure and navigate along the inspection route, while collecting additional data for increasing the accuracy and improving the reliability of future inspections. A key innovation is making information available in multiple forms and levels of abstraction so as to meet the often-conflicting needs of offline (e.g., visualization of inspection areas and planning information-rich paths) and online (e.g., map-based localization and obstacle avoidance) uses. Also critical is an information-driven approach for making maximum use of the limited sensing and processing resources available to the quadrotor. Lastly, a key advantage of the proposed approach is that it provides the foundation for continual improvement in accuracy and efficiency after each inspection flight.