Frank Bieder

M.Sc. Frank Bieder

  • FZI Forschungszentrum Informatik
    Haid-und-Neu-Str. 10–14
    76131 Karlsruhe, Germany

Research

My research interest lies at the intersection of computer vision and machine learning in the context of mobile perception systems. Currently, I am working on deep learning methods for 3D scene understanding to enable map-less driving or self-localization in sparse planning maps. In particular, I am interested in exploring how to overcome data shortage by leveraging the 6D pose awareness of mobile systems in highly accurate semantic 3D maps . Exampels of my current research are:

  • Map Learning: Automatic data generation for deep learning applications using HD maps and multi-drive mapping
  • Leveraging Map Learning for self-localisation, semantic mapping and map-less driving
  • Learning-based 3D scene understanding

Teaching

  • Übungsleiter der Vorlesung Messtechnik II (SS20, WS20/21, SS21, WS21/22, SS22, WS22/23)
  • Aufgabensteller Grundlagen der Mess- und Regelungstechnik (SS19, WS19/20, SS20, SS21, WS21/22, WS22/23)
  • Mechatronik-Praktikum (WS19/20)
  • Rechnergestützte Verfahren in der Mess- und Regelungstechnik (WS19/20, WS20/21, WS21/22)

Academic Services

  • Reviewer for
    • IEEE ITS Intelligent Vehicles Symposium (IV)
    • IEEE ITS Intelligent Transportation Systems Magazine (ITS-M)
    • IEEE RAS International Conference for Robotics and Automatisation (ICRA)
    • IEEE RSJ International Conference on Intelligent Robots and Systems (IROS)
    • IEEE RAS Robotics and Automation Letters (RA-L)
    • Springer International Journal of Computer Vision (VISI)
    • De Gruyter TM-Technisches Messen (TM)
  • Chair of workshop on Bridging the gap between map-based and map-less driving in the context of automated driving at Intelligent Vehicles Symposium (IV) 2022 in Aachen, Germany, and 2023 in Anchorage, USA.
  • Associate editor at Intelligent Vehicles Symposium (IV) 2022 in Anchorage, USA
  • Chair of special session on Real-time critical perception tasks in the context of automated driving at FUSION 2021 in Sun City, South Africa, and FUSION 2022 in Linköping, Sweden

Open Theses

Completed Theses

  • Recursive fusion of sequential LiDAR measurements considering dynamic occlusions for the creation of grid maps in the context of autonomous driving, master thesis March 2022
  • Automated Data Generation with HD-Maps for Machine Learning in the Context of Automated Driving, master thesis, April 2021
  • Occlusion Handling for Automatic Data Generation using HD Maps and a highly accurate SLAM, master thesis, November 2020
  • Fusion of Simultaneously Learned Semantic Information from Different Representations, master thesis, November 2020
  • A Comparison of Different Approaches to Solve the SLAM Problem on a Formula Student Driverless Race Car, master thesis, November 2020
  • Combining Sequential LiDAR Measurements For Semantic Segmentation of Multi-Layer Grid Maps, master thesis, November 2020
  • Panoptic Segmentation of Urban Scenarios, master thesis, September 2020

Publications