Proceeding of: 14th International Conference, ACIVS 2012, Brno, Czech Republic, September 4-7, 2012 ; The movement of the vehicle is an useful information for different applications, such as driver assistant systems or autonomous vehicles. This information can be known by different methods, for instance, by using a GPS or by means of the visual odometry. However, there are some situations where both methods do not work correctly. For example, there are areas in urban environments where the signal of the GPS is not available, as tunnels or streets with high buildings. On the other hand, the algorithms of computer vision are affected by outdoor environments, and the main source of difficulties is the variation in the ligthing conditions. A method to estimate and predict the movement of the vehicle based on visual odometry and Kalman filter is explained in this paper. The Kalman filter allows both filtering and prediction of vehicle motion, using the results from the visual odometry estimation. ; This work was also supported by Spanish Government through the CICYT projects FEDORA (Grant TRA2010-20255-C03-01), Driver Distraction Detector System (Grant TRA2011-29454-C03-02) and by CAM through the projects SEGVAUTO-II. ; Publicado


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Estimation and prediction of the vehicle's motion basedon visual odometry and Kalman filter



    Erscheinungsdatum :

    2012-01-01



    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Robust Visual-Inertial Odometry Based on a Kalman Filter and Factor Graph

    Wang, Zhiwei / Pang, Bao / Song, Yong et al. | IEEE | 2023



    Visual Odometry

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


    Visual odometry

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


    Uncertainty Estimation for Stereo Visual Odometry

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