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27 March 2019 Vision-based precision localization of UAVs for sensor payload placement and pickup for field monitoring applications
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Due to their mobility and autonomy, unmanned aerial vehicles (UAVs) provide an unprecedented opportunity to perform data gathering in a wide array of civil engineering applications such as visual inspection of infrastructure. Given their versatility, the role of UAVs can be expanded by leveraging their autonomous operations to deploy wireless sensing resources. This can be especially valuable in numerous field applications such as shear wave velocity (Vs) assessment of the subsurface. This study explores the feasibility of automating the autonomous placement and pickup of wireless geophone sensors using UAVs for multichannel analysis of surface waves (MASW) for subsurface characterization. Typically, autonomous navigation of UAVs is based on the fusion of inertial sensors and GPS to control the UAV flight trajectory. However, this approach is not sufficiently accurate for missions requiring precision placement and pickup of payloads (such as sensors). Hence, computer vision using fiducial markers can be used to augment traditional inertial sensing to add accuracy to the localization of the UAV relative to payloads. In this study, we use a set of fiducial markers of varying sizes as tracking targets during navigation missions. Pose information extracted from the marker images are integrated into a sensor fusion controller based on the Kalman filter. The work conducts field validation of the proposed computer vision navigation method showing accuracy of the UAV landing on a user defined target within 10 cm; as the UAV descends, smaller fiducial markers are shown to increase the precision of the UAV placement on the ground.
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Hao Zhou, Jerome Lynch, and Dimitrios Zekkos "Vision-based precision localization of UAVs for sensor payload placement and pickup for field monitoring applications", Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, 1097007 (27 March 2019);

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