Robotics

  • 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
  • Semantic Segmentation of Point Clouds using Semi Supervised Transfer Learning
    Semantic segmentation in 3d describes a point wise classification of point clouds. We think this task is challenging because on one side it is hard for humans to annotate the necessary data, because objects may appear ambiguous and labelling in 3d can be time consuming. On the other hand it appears that there is still no preferred way of how the data should be processed in order to use it with deep neural networks.
    Leaders: Brenner
    Team: Peters, Brenner
    Year: 2017
    Sponsors: DFG-Graduiertenkolleg i.c.sens
  • 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
    Sponsors: DFG-Graduiertenkolleg i.c.sens