Visual Odometry (VO) estimation is an important source of information for vehicle state estimation and autonomous driving. Recently, deep learning based approaches have begun to appear in the literature. However, in the context of driving, single sensor based approaches are often prone to failure because of degraded image quality due to environmental factors, camera placement, etc. To address this issue, we propose a deep sensor fusion framework which estimates vehicle motion using both pose and uncertainty estimations from multiple onboard cameras. We extract spatio-temporal feature representations from a set of consecutive images using a hybrid CNN - RNN model. We then utilise a Mixture Density Network (MDN) to estimate the 6-DoF pose as a mixture of distributions and a fusion module to estimate the final pose using MDN outputs from multi-cameras. We evaluate our approach on the publicly available, large scale autonomous vehicle dataset, nuScenes. The results show that the proposed fusion approach surpasses the state-of-the-art, and provides robust estimates and accurate trajectories compared to individual camera-based estimations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-Camera Sensor Fusion for Visual Odometry using Deep Uncertainty Estimation


    Contributors:


    Publication date :

    2021-09-19


    Size :

    1548355 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Uncertainty Estimation for Stereo Visual Odometry

    Ross, Derek / De Petrillo, Matteo / Strader, Jared et al. | British Library Conference Proceedings | 2021


    Uncertainty-Aware Attention Guided Sensor Fusion For Monocular Visual Inertial Odometry

    Shinde, Kashmira | German Aerospace Center (DLR) | 2020

    Free access

    Robust visual odometry using uncertainty models

    Van Hamme, David / Veelaert, Peter / Philips, Wilfried | BASE | 2011

    Free access

    Deep Direct Visual Odometry

    Zhao, Chaoqiang / Tang, Yang / Sun, Qiyu et al. | IEEE | 2022


    4DRVO-Net: Deep 4D Radar–Visual Odometry Using Multi-Modal and Multi-Scale Adaptive Fusion

    Zhuo, Guirong / Lu, Shouyi / Zhou, Huanyu et al. | IEEE | 2024