Runway Detection and Tracking for Autonomous Landing of a UAV [video]
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This presentation outlines an approach to develop a robust, real-time runway detection and tracking system for an unmanned aerial vehicle (UAV) using computer vision and standard guidance, navigation, and control (GNC) equipment. Feature extraction techniques, specifically the Hough transform, are used to identify the position and orientation of a runway from cameras on board an aircraft. Information relating the position of the aircraft and the runway is then integrated into a control system for the final approach and landing stages of UAV flight. While conceptual in nature, this presentation utilizes modeling and simulation to demonstrate the feasibility of a computer vision approach to an autoland capability for a UAV. This approach could also have potential applications in other autonomous fields by illustrating the practicality of vision-based feedback for guidance and control of unmanned autonomous systems.
TechCon2017 (CRUSER)Presented by ENS Tyler McCarthy, USN: NPS Systems EngineeringIncludes slides
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