• Collaborative acquisition of predictive maps
    Self-driving cars and robots that run autonomously over long periods of time need high precision and up-to-date models of the environment. Natural environments contain dynamic objects and change over time. Since a permanent observation of “everything” is impossible and there will always be a first time visit of the changed area, a map that takes into account the possibility of change is needed.
    Team: Schachtschneider, Brenner
    Year: 2017
    Funding: DFG-Graduiertenkolleg i.c.sens
  • Landmark-based localization
    Within the project, new approaches are developed for a highly accurate localization of vehicles relative to their environment. Furthermore, it is analyzed how detailed descriptions of the environment can be used for interpreting the scenery, for example for active driver assistance systems
    Team: Schlichting, Brenner
    Year: 2017