Millimeter-Wave Radar is one promising sensor to achieve robust perception against challenging observing conditions. In this paper, we propose a Radar Inertial Odometry (RIO) pipeline utilizing a long-range 4D millimeter-wave radar for autonomous vehicle navigation. Initially, we develop a perception frontend based on radar point cloud filtering and registration to estimate the relative transformations between frames reliably. Then an optimization-based backbone is formulated, which fuses IMU data, relative poses, and point cloud velocities from radar Doppler measurements. The proposed method is extensively tested in challenging on-road environments and in-the-air environments. The results indicate that the proposed RIO can provide a reliable localization function for mobile platforms, such as automotive vehicles and Unmanned Aerial Vehicles (UAVs), in various operation conditions.


    Access

    Download


    Export, share and cite



    Title :

    Robust Radar Inertial Odometry in Dynamic 3D Environments


    Contributors:
    Yang Lyu (author) / Lin Hua (author) / Jiaming Wu (author) / Xinkai Liang (author) / Chunhui Zhao (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    RADAR AIDED VISUAL INERTIAL ODOMETRY INITIALIZATION

    NIESEN URS | European Patent Office | 2019

    Free access

    Radar aided visual inertial odometry initialization

    NIESEN URS | European Patent Office | 2020

    Free access

    RADAR AIDED VISUAL INERTIAL ODOMETRY OUTLIER REMOVAL

    NIESEN URS | European Patent Office | 2019

    Free access

    Radar aided visual inertial odometry outlier removal

    NIESEN URS | European Patent Office | 2019

    Free access

    R3O: Robust Radon Radar Odometry

    Lubanco, Daniel Louback S. / Hashem, Ahmed / Pichler-Scheder, Markus et al. | IEEE | 2024

    Free access