This work explores Doppler information from a millimetre-Wave (mm-W) Frequency-Modulated Continuous-Wave (FMCW) scanning radar to make odometry estimation more robust and accurate. Firstly, doppler information is added to the scan masking process to enhance correlative scan matching. Secondly, we train a Neural Network (NN) for regressing forward velocity directly from a single radar scan; we fuse this estimate with the correlative scan matching estimate and show improved robustness to bad estimates caused by challenging environment geometries, e.g. narrow tunnels. We test our method with a novel custom dataset which is released with this work at https://ori.ox.ac.uk/publications/datasets.


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    Titel :

    Doppler-Aware Odometry from FMCW Scanning Radar


    Beteiligte:
    Rennie, Fraser (Autor:in) / Williams, David (Autor:in) / Newman, Paul (Autor:in) / De Martini, Daniele (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    4213155 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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