2020 · 67 citations · 15 references
ComputingAutonomous SystemsComputer ScienceComputer VisionRobot VisionUnmanned Aircraft SystemsImage RegistrationOutdoor LocalizationPose EstimationRobot LocalizationVisual LocalizationVisual Odometry3D Pose EstimationFeature-based Visual OdometryFilteringFlightLocal LocalizationLocalization In Gps-denied EnvironmentsLocalization RobustnessRobust Pose Estimation
We estimate the global pose of a multirotor UAV by visually localizing images captured during a flight with Google Earth images pre-rendered from known poses. We metrically localize real images with georeferenced rendered images using a dense mutual information technique to allow accurate global pose estimation in outdoor GPS-denied environments. We show the ability to consistently localize throughout a sunny summer day despite major lighting changes while demonstrating that a typical feature-based localizer struggles under the same conditions. Successful image registrations are used as measurements in a filtering framework to apply corrections to the pose estimated by a gimballed visual odometry pipeline. We achieve less than 1 m and 1° RMSE on a 303 m flight and less than 3 m and 3° RMSE on six 1132 m flights as low as 36 m above ground level conducted at different times of the day from sunrise to sunset.
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