In this paper, we propose an interoceptive-only odometry system for ground robots with neural network processing and soft constraints based on the assumption of a globally continuous ground manifold. Exteroceptive sensors such as cameras, GPS and LiDAR may encounter difficulties in scenarios with poor illumination, indoor environments, dusty areas and straight tunnels. Therefore, improving the pose estimation accuracy only using interoceptive sensors is important to enhance the reliability of navigation system even in degrading scenarios mentioned above. However, interoceptive sensors like IMU and wheel encoders suffer from large drift due to noisy measurements. To overcome these challenges, the proposed system trains deep neural networks to correct the measurements from IMU and wheel encoders, while considering their uncertainty. Moreover, because ground robots can only travel on the ground, we model the ground surface as a globally continuous manifold using a dual cubic B-spline manifold to further improve the estimation accuracy by this soft constraint. A novel space-based sliding-window filtering framework is proposed to fully exploit the $C^{2}$ continuity of ground manifold soft constraints and fuse all the information from raw measurements and neural networks in a yaw-independent attitude convention. Extensive experiments demonstrate that our proposed approach can outperform state-of-the-art learning-based interoceptive-only odometry methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    WING: Wheel-Inertial Neural Odometry With Ground Manifold Constraints


    Contributors:
    Jiang, Chenxing (author) / Zhang, Kunyi (author) / Yang, Sheng (author) / Shen, Shaojie (author) / Xu, Chao (author) / Gao, Fei (author)

    Published in:

    Publication date :

    2025-03-01


    Size :

    3676896 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    GROUND VEHICLE MONOCULAR VISUAL-INERTIAL ODOMETRY VIA LOCALLY FLAT CONSTRAINTS

    RAMIREZ LLANOS EDUARDO JOSE / YU XIN / VERMA DHIREN | European Patent Office | 2022

    Free access


    Learning-Aided Inertial Odometry With Nonlinear State Estimator on Manifold

    Jin, Yuqiang / Zhang, Wen-An / Sun, Hu et al. | IEEE | 2023


    WHEEL ODOMETRY AIDED VISUAL-INERTIAL ODOMETRY FOR LAND VEHICLE NAVIGATION IN WINTER URBAN ENVIRONMENTS

    Huang, Cheng / Jiang, Yang / O Keefe, Kyle | British Library Conference Proceedings | 2020


    Monocular Visual-Inertial-Wheel Odometry Using Low-Grade IMU in Urban Areas

    Jung, Jae Hyung / Cha, Jaehyuck / Chung, Jae Young et al. | IEEE | 2022