{"id":{"repo_id":"byu","oai_identifier":"oai:scholarsarchive.byu.edu:etd-2417"},"canonical_url":"https://search.dev.ndltd.org/etd/byu/oai:scholarsarchive.byu.edu:etd-2417","repository":{"repo_id":"byu","name":"Brigham Young University","base_url":"https://scholarsarchive.byu.edu/do/oai/"},"display":{"title":"Real-Time Implementation of Vision Algorithm for Control, Stabilization, and Target Tracking for a Hovering Micro-UAV","abstract":"A lightweight, powerful, yet efficient quad-rotor platform was designed and constructed to obtain experimental results of completely autonomous control of a hovering micro-UAV using a complete on-board vision system. The on-board vision and control system is composed of a Helios FPGA board, an Autonomous Vehicle Toolkit daughterboard, and a Kestrel Autopilot. The resulting platform is referred to as the Helio-copter. An efficient algorithm to detect, correlate, and track features in a scene and estimate attitude information was implemented with a combination of hardware and software on the FPGA, and real-time performance was obtained. The algorithms implemented include a Harris feature detector, template matching feature correlator, RANSAC similarity-constrained homography, color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This implementation was designed specifically for use as an on-board vision solution in determining movement of small unmanned air vehicles that have size, weight, and power limitations. Experimental results show the Helio-copter capable of maintaining level, stable flight within a 6 foot by 6 foot area for over 40 seconds without human intervention.","abstract_html":"A lightweight, powerful, yet efficient quad-rotor platform was designed and constructed to obtain experimental results of completely autonomous control of a hovering micro-UAV using a complete on-board vision system. The on-board vision and control system is composed of a Helios FPGA board, an Autonomous Vehicle Toolkit daughterboard, and a Kestrel Autopilot. The resulting platform is referred to as the Helio-copter. An efficient algorithm to detect, correlate, and track features in a scene and estimate attitude information was implemented with a combination of hardware and software on the FPGA, and real-time performance was obtained. The algorithms implemented include a Harris feature detector, template matching feature correlator, RANSAC similarity-constrained homography, color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This implementation was designed specifically for use as an on-board vision solution in determining movement of small unmanned air vehicles that have size, weight, and power limitations. 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Fulton College of Engineering and Technology; Electrical and Computer Engineering"]},{"key":"dc:description.abstract","label":"Abstract","values":["A lightweight, powerful, yet efficient quad-rotor platform was designed and constructed to obtain experimental results of completely autonomous control of a hovering micro-UAV using a complete on-board vision system. The on-board vision and control system is composed of a Helios FPGA board, an Autonomous Vehicle Toolkit daughterboard, and a Kestrel Autopilot. The resulting platform is referred to as the Helio-copter. An efficient algorithm to detect, correlate, and track features in a scene and estimate attitude information was implemented with a combination of hardware and software on the FPGA, and real-time performance was obtained. The algorithms implemented include a Harris feature detector, template matching feature correlator, RANSAC similarity-constrained homography, color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This implementation was designed specifically for use as an on-board vision solution in determining movement of small unmanned air vehicles that have size, weight, and power limitations. Experimental results show the Helio-copter capable of maintaining level, stable flight within a 6 foot by 6 foot area for over 40 seconds without human intervention."]},{"key":"dc:format","label":"Dc Format","values":["application:pdf"]},{"key":"dc:source","label":"Dc Source","values":["Brigham Young University - Provo"]},{"key":"dc:title","label":"Title","values":["Real-Time Implementation of Vision Algorithm for Control, Stabilization, and Target Tracking for a Hovering Micro-UAV"]}]}],"canonical_facts":{"dc:creator":["Tippetts, Beau J."],"dc:date":["2008-04-23T07:00:00Z"],"dc:description":["Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering"],"dc:description.abstract":["A lightweight, powerful, yet efficient quad-rotor platform was designed and constructed to obtain experimental results of completely autonomous control of a hovering micro-UAV using a complete on-board vision system. The on-board vision and control system is composed of a Helios FPGA board, an Autonomous Vehicle Toolkit daughterboard, and a Kestrel Autopilot. The resulting platform is referred to as the Helio-copter. An efficient algorithm to detect, correlate, and track features in a scene and estimate attitude information was implemented with a combination of hardware and software on the FPGA, and real-time performance was obtained. The algorithms implemented include a Harris feature detector, template matching feature correlator, RANSAC similarity-constrained homography, color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This implementation was designed specifically for use as an on-board vision solution in determining movement of small unmanned air vehicles that have size, weight, and power limitations. Experimental results show the Helio-copter capable of maintaining level, stable flight within a 6 foot by 6 foot area for over 40 seconds without human intervention."],"dc:format":["application:pdf"],"dc:identifier":["https://scholarsarchive.byu.edu/etd/1418","https://scholarsarchive.byu.edu/context/etd/article/2417/viewcontent/ETD_CISOPTR_1422.pdf"],"dc:language":["English"],"dc:publisher":["Brigham Young University - Provo"],"dc:source":["Brigham Young University - Provo"],"dc:subject":["real-time image processing","micro-UAV","control","quad-rotor","on-board","vision system","target tracking","BYU","FPGA","RANSAC","similarity-constrained homography","template matching","Helio-copter","Electrical and Computer Engineering"],"dc:title":["Real-Time Implementation of Vision Algorithm for Control, Stabilization, and Target Tracking for a Hovering Micro-UAV"],"dc:type":["Thesis"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T01:29:12Z"}