In this paper, the commonly used filtering technique occupancy grid mapping for static environments is extended for dynamic environments. The proposed method is able to estimate velocities indirectly. We apply a distribution model of the respective state variable to estimate the cell dynamics by means of prediction and update cycle, as known by standard tracking filters. Therefore, we present a straight forward derivation of the prediction and update rule. Furthermore, we validate our approach by simple one dimensional simulations, and show how it can be extended into a two dimensional world, including the resulting consequences, e.g. in terms of memory requirements.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Extending occupancy grid mapping for dynamic environments


    Beteiligte:


    Erscheinungsdatum :

    2018-06-01


    Format / Umfang :

    756792 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Radar-based Dynamic Occupancy Grid Mapping and Object Detection

    Diehl, Christopher / Feicho, Eduard / Schwambach, Alexander et al. | IEEE | 2020


    Environment Recognition with FMCW-LiDAR-based Dynamic Occupancy Grid Mapping

    Okuya, Tsubasa / Yoneda, Masaki / Ogawa, Takashi | IEEE | 2023


    Efficient dynamic occupancy grid mapping using non-uniform cell representation

    Buerkle, Cornelius / Oboril, Fabian / Jarquin, Julio et al. | IEEE | 2020


    Bayesian Occupancy grid Filter for dynamic environments using prior map knowledge

    Gindele, Tobias / Brechtel, Sebastian / Schroder, Joachim et al. | IEEE | 2009


    Bayesian Occupancy Grid Filter for Dynamic Environments Using Prior Map Knowledge

    Gindele, T. / Brechtel, S. / Schroder, J. et al. | British Library Conference Proceedings | 2009