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dc.contributor.authorChowdhary, Girish
dc.contributor.authorJohnson, Eric N.
dc.contributor.authorMagree, Daniel
dc.contributor.authorWu, Allen
dc.contributor.authorShein, Andy
dc.date.accessioned2014-09-02T15:00:12Z
dc.date.available2014-09-02T15:00:12Z
dc.date.issued2013-05
dc.identifier.citationChowdhary, G., Johnson, E. N., Magree, D., Wu, A., and Shein, A., “Gps-denied indoor and outdoor monocular vision aided navigation and control of unmanned aircraft,” Journal of Field Robotics, vol. 30, no. 3, pp. 415–438, 2013. DOI: 10.1002/rob.21454en_US
dc.identifier.urihttp://hdl.handle.net/1853/52339
dc.descriptionCopyright © 2013 Wileyen_US
dc.descriptionDOI: http://dx.doi.org/10.1002/rob.21454
dc.description.abstractGPS-denied closed-loop autonomous control of unstable Unmanned Aerial Vehicles (UAVs) such as rotorcraft using information from a monocular camera has been an open problem. Most proposed Vision aided Inertial Navigation Systems (V-INSs) have been too computationally intensive or do not have sufficient integrity for closed-loop flight. We provide an affirmative answer to the question of whether V-INSs can be used to sustain prolonged real-world GPS-denied flight by presenting a V-INS that is validated through autonomous flight-tests over prolonged closed-loop dynamic operation in both indoor and outdoor GPS-denied environments with two rotorcraft unmanned aircraft systems (UASs). The architecture efficiently combines visual feature information from a monocular camera with measurements from inertial sensors. Inertial measurements are used to predict frame-to-frame transition of online selected feature locations, and the difference between predicted and observed feature locations is used to bind in real-time the inertial measurement unit drift, estimate its bias, and account for initial misalignment errors. A novel algorithm to manage a library of features online is presented that can add or remove features based on a measure of relative confidence in each feature location. The resulting V-INS is sufficiently efficient and reliable to enable real-time implementation on resource-constrained aerial vehicles. The presented algorithms are validated on multiple platforms in real-world conditions: through a 16-min flight test, including an autonomous landing, of a 66 kg rotorcraft UAV operating in an unconctrolled outdoor environment without using GPS and through a Micro-UAV operating in a cluttered, unmapped, and gusty indoor environment.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectVision aided Inertial Navigation Systems (V-INS)en_US
dc.subjectAutonomous vehiclesen_US
dc.subjectGPS-denied dynamic environmenten_US
dc.titleGPS-denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraften_US
dc.typePost-printen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Aerospace Engineeringen_US
dc.contributor.corporatenameCyPhy Works Inc.en_US
dc.identifier.doi10.1002/rob.21454
dc.embargo.termsnullen_US


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