Hello, I am Niclas!

Ph.D. Student, Researcher, and Sports Enthusiast

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  • Profile picture
  • Name: Niclas Vödisch
  • Pronouns: He/Him/His
  • PhD School: University of Freiburg
  • Alma Mater: ETH Zurich
  • Interests: Autonomous Driving, Deep Learning, Computer Vision, Software Engineering

I am a Ph.D. student at the Autonomous Intelligent Systems lab at the University of Freiburg and a member of the ELLIS Society. I am supervised by Prof. Dr. Wolfram Burgard and co-supervised by Prof. Dr. Abhinav Valada. My interests lie in boosting machine perception as well as SLAM systems with deep learning methods. To this end, I am mostly focusing on mobile robotics and, in particular, on autonomous driving.

I obtained my M.Sc. degree in Computational Science and Engineering at ETH Zurich, where I wrote my Master’s Thesis at the Computer Vision Lab. During my time in Zurich, I was a member of AMZ Driverless, the most successful Formula Student Driverless team in the world. In my first season 2019, I worked on the vehicle’s perception system integrating multiple LiDARs and Sensor Fusion in our pipeline. Afterward, I took over the role as CTO leading an international team of approximately 20 Master’s students. I further interned at the sensor fusion team of AID, a former Audi subsidiary acquired by Argo AI, working on LiDAR-based mapping.

Previously, I obtained a B.Sc. in Computational Engineering Science at RWTH Aachen University and worked as an undergraduate research assistant in the automated driving group of the Institute for Automotive Engineering. In 2016/17, I spent two semesters as a visiting student at Carnegie Mellon University in Pittsburgh, PA, taking classes in Chemical Engineering and Machine Learning.

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My Education

  • University of Freiburg

    Ph.D. Student | June 2021 - Present

    Program: Computer Science

    Membership: ELLIS Society

  • ETH Zurich

    Master of Science (M.Sc.) | September 2018 - May 2021

    Program: Computational Science and Engineering (CSE)

    Thesis: Optimizing the Beam Distribution of a Low-Resolution LiDAR for 3D Localization

  • Carnegie Mellon University

    Visiting undergraduate student | August 2016 - May 2017

    Awards: Dean's List (fall 2016)

    Funding: DAAD full scholarship (Sept 2016 - May 2017)

  • RWTH Aachen University

    Bachelor of Science (B.Sc.) | September 2014 - June 2018

    Program: Computational Engineering Science (CES)

    Thesis: Design, Implementation, and Evaluation of a System for Optimizing a Scenario Detector for Highly Automated Vehicles

My Experience

  • AMZ Driverless

    Formula Student Driverless Team of ETH Zurich CTO | Zurich, Switzerland | August 2019 - August 2020

    • I led an international team of approx. 20 master’s students to ensure the technical progress of the project.
    • Despite restrictions due to COVID-19, cancellation of the competitions, and working remotely for several months, we managed to have a running autonomous race car and pushed our on-track performance to the next level.

    Perception Engineer | Zurich, Switzerland | October 2018 - August 2019

    • Driverless champions at FS Germany 2019 and FS East 2019.
    • I worked on the LiDAR pipeline and extended it by a sensor fusion approach with RGB images.

  • AID (acquired by Argo AI)

    Intern - Sensor Fusion Team | Munich, Germany | September 2019 - February 2020

    • I created a globally consistent 3D map from LiDAR and GNSS data using a GraphSLAM-based approach.
    • The method helped to verify existing localization methods as well as LiDAR-to-LiDAR calibration, and provided great material for virtual reality walks.

  • Robert Bosch GmbH

    Intern - Automated Driving Team | Renningen, Germany | April 2018 - July 2018

    • I worked on a DL-based method to predict the future path of vehicles approaching an intersection that is equipped with smart infrastructure to detect cars.

  • fka GmbH

    Student Research Assistant - Automated Driving Group | Aachen, Germany | April 2018 - July 2018

  • Institute for Automotive Engineering (ika) at RWTH Aachen University

    Student Research Assistant - Automated Driving Group | Aachen, Germany | August 2017 - March 2018

    • I worked on various tasks including developing a web interface for monitoring the in-house GPU cluster.

My Publications

Click on any project tile to learn more. The asterisk (*) denotes equal contribution.

  • BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation

    J. Schramm, N. Vödisch*, K. Petek, B R. Kiran, S. Yogamani, W. Burgard, and A. Valada | arXiv preprint arXiv:2403.11761, 2024

  • Few-Shot Panoptic Segmentation With Foundation Models

    M. Käppeler*, K. Petek*, N. Vödisch*, W. Burgard, and A. Valada | International Conference on Robotics and Automation (ICRA), 2024

  • Collaborative Dynamic 3D Scene Graphs for Automated Driving

    E. Greve*, M. Büchner*, N. Vödisch*, W. Burgard, and A. Valada | International Conference on Robotics and Automation (ICRA), 2024

  • CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation

    N. Vödisch*, K. Petek*, W. Burgard, and A. Valada | Robotics: Science and Systems (RSS), 2023

  • CoVIO: Online Continual Learning for Visual-Inertial Odometry

    N. Vödisch, D. Cattaneo, W. Burgard, and A. Valada | Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023

  • PADLoC: LiDAR-Based Deep Loop Closure Detection and Registration Using Panoptic Attention

    J. Arce, N. Vödisch, D. Cattaneo, W. Burgard, and A. Valada | Robotics and Automation Letters (RA-L), vol. 8, issue 3, pp. 1319-1326, March 2023

  • Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping through Continual Learning

    N. Vödisch, D. Cattaneo, W. Burgard, and A. Valada | International Symposium on Robotics Research (ISRR), 2022

  • End-to-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization

    N. Vödisch, O. Unal, K. Li, L. Van Gool, and D. Dai | Robotics and Automation Letters (RA-L), vol. 7, issue 2, pp. 2242-2249, April 2022

  • FSOCO: The Formula Student Objects in Context Dataset

    N. Vödisch*, D. Dodel*, and M. Schötz* | SAE International Journal of Connected and Automated Vehicles, vol. 5, 2022

  • Accurate Mapping and Planning for Autonomous Racing

    L. Andresen*, A. Brandemuehl*, A. Hönger*, B. Kuan*, N. Vödisch*, H. Blum, V. Reijgwart, L. Bernreiter, L. Schaupp, J. J. Chung, M. Bürki, M. R. Oswald, R. Siegwart, and A. Gawel | International Conference on Intelligent Robots and Systems (IROS), 2020

Invited Talks

  • Continual Learning for Robotics

    Summer School on Deep Learning for Autonomous Systems and Smart Cities, Aarhus University | Aarhus, Denmark | May 2023

  • Formula Student Driverless: Autonomous Driving at the Limit

    Seminar on Vehicles and Engine Technology, TU Darmstadt | Darmstadt, Germany (online) | May 2021

  • ML in Sensing – Benefits and Drawbacks

    FSG Academy Waymo | Hockenheim, Germany | August 2020

  • Dealing with Uncertainties in a Multi-Sensor Perception Setup

    Formula Student Symposium | Győr, Hungary | November 2019

  • The FSD Winning Car

    FSG Academy Magna | Untergruppenbach, Germany | November 2019