Recent developments of UAVs have sparked interest in building UAV-based magnetic surveying systems. Due to the sensitivity of magnetic sensors, direct positioning the magnetometer via traditional devices, e.g., GNSS and UWB, is not possible. However, to get obtain georeferenced data, it is necessary to localize the payload accurately. This paper presents the sensor fusion technique used for the development of an accurate vision-based positioning solution. The sensor fusion module builds upon a square-root unscented Kalman filter to fuse the relative positioning results computed by a monocular camera with the global positioning results from a GNSS/IMU system to determine a target's global positions. The developed method has been validated by real flight experiments. Experimental results show it can provide accurate position estimations, achieving single-axis centimeter-level positioning accuracy and decimeter-level overall 3D positioning accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vision-Aided State Estimator for Positioning UAVs


    Contributors:
    Hu, Xiao (author) / Olesen, Daniel (author) / Knudsen, Per (author)


    Publication date :

    2021-06-15


    Size :

    5001698 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vision-based Positioning for UAVs

    Hu, Xiao | BASE | 2021

    Free access

    Vision-Aided Navigation for Small UAVs in GPS-Challenged Environments

    Madison, R. / Andrews, G. / DeBitetto, Paul et al. | AIAA | 2007


    Formation Control of Quadrotor UAVs by Vision-Based Positioning

    Kim, Wooh Yun ;Kwon, Ji Wook ;Seo, Ji Won | Trans Tech Publications | 2015


    Vision-Aided Measurement Level Integration of Multiple GPS Receivers for UAVs

    Shetty, Akshay / Gao, Grace Xingxin | British Library Conference Proceedings | 2015


    Homography-based Vision-aided Inertial Navigation of UAVs in Unknown Environments

    Zhao, Shiyu / Lin, Feng / Peng, Kemao et al. | AIAA | 2012