New methods based on vision have emerged in the area of mobile vehicle localization. Such methods offer an improved alternative in terms of accuracy to traditional localization methods like wheel odometry. In this paper we propose such a method that does not compromise precision and can run in real time. Depending on environment, feature numbers are sometimes insufficient. To solve this, our algorithm allows using slower feature detectors like SURF for frame keypoints, together with Shi-Tomasi corners for increasing points number. We show how accuracy is further improved by using a Kalman filter to enhance the computation of pose to pose relative motion variation.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Improving localization accuracy based on Lightweight Visual Odometry


    Beteiligte:
    Pojar, D (Autor:in) / Pangyu Jeong, (Autor:in) / Nedevschi, S (Autor:in)


    Erscheinungsdatum :

    01.09.2010


    Format / Umfang :

    844480 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Lightweight Deep Learning Algorithm for Visual Odometry

    Zhang, Dingnan / Peng, Tao / Liu, Ruixu et al. | IEEE | 2023


    Selective visual odometry for accurate AUV localization

    Bellavia, F. | British Library Online Contents | 2017


    Visual odometry

    Nister, D. / Naroditsky, O. / Bergen, J. | IEEE | 2004


    Visual Odometry

    Nister, D. / Naroditsky, O. / Bergen, J. et al. | British Library Conference Proceedings | 2004


    Stereo Visual Odometry for Indoor Localization of Ship Model

    Kučić, Mario / Valčić, Marko | BASE | 2020

    Freier Zugriff