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Niclas Vödisch

Albert-Ludwigs-Universität Freiburg
Technische Fakultät
Autonome Intelligente Systeme
Georges-Köhler-Allee 080
D-79110 Freiburg i. Br., Germany
Office:   080-00-022

Email: voedisch@cs.uni-freiburg.de
Website: vniclas.github.io

About me

  • 06/2021 - present: Working at the Autonomous Intelligent Systems group of Prof. Dr. Wolfram Burgard as Ph.D. student; collaborating with the Robot Learning group led by Prof. Dr. Abhinav Valada.
  • 09/2018 - 05/2021: M.Sc. in Computational Science and Engineering at ETH Zurich, Switzerland
  • 08/2016 - 05/2017: Visiting student at Carnegie Mellon University in Pittsburgh, PA, USA
  • 09/2014 - 06/2018: B.Sc. in Computational Engineering Science at RWTH Aachen University, Germany

Research Interests

  • Continual Learning for Robotics
  • Machine Perception
  • Simultaneous Localization and Mapping (SLAM)

Research Projects



  • Label-Efficient Panoptic Segmentation With Self-Supervised Vision Foundation Models, Markus Käppeler, Master Thesis, ongoing (with Kürsat Petek)
  • BEVCar: Camera-Radar BEV Object and Semantic Map Segmentation, Jonas Schramm, Master Thesis, ongoing (with Kürsat Petek)
  • Mapping, Navigation, and Control on a Real-World Autonomous Platform, Tim Steinke, Master Project, 09/2023 (with Martin Büchner)
  • COBUS: Collaborative Urban Scene Graph Generation Using Long-Term Panoptic SLAM, Elias Greve, Master Thesis, 07/2023 (with Martin Büchner)
  • Cross-Modal Distillation for Multi-Camera 3D Object Detection and BEV Map Segmentation, Markus Käppeler, Master Project, 03/2023 (with Kürsat Petek)
  • PADLoC: Deep Loop Closure Detection and Registration Using Panoptic Attention, José Arce y de la Borbolla, Master Thesis, 06/2022 (with Daniele Cattaneo)



  • M. Käppeler*, K. Petek*, N. Vödisch*, W. Burgard, and A. Valada
    Few-Shot Panoptic Segmentation With Foundation Models
    arXiv preprint arXiv:2309.10726, 2023
    arXiv Website BibTeX
  • E. Greve*, M. Büchner*, N. Vödisch*, W. Burgard, and A. Valada
    Collaborative Dynamic 3D Scene Graphs for Automated Driving
    arXiv preprint arXiv:2309.06635, 2023
    arXiv Website BibTeX

Workshop Papers

  • N. Vödisch, D. Cattaneo, W. Burgard, and A. Valada
    CoVIO: Online Continual Learning for Visual-Inertial Odometry
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023
    arXiv IEEE Xplore Website BibTeX

Conference Papers

  • N. Vödisch*, K. Petek*, W. Burgard, and A. Valada
    CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation
    Robotics: Science and Systems (RSS), Daegu, South Korea, 2023
    arXiv RSS Video Website BibTex
  • N. Vödisch, D. Cattaneo, W. Burgard, and A. Valada
    Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping through Continual Learning
    International Symposium on Robotics Research (ISRR), Geneva, Switzerland, 2022
    arXiv Springer Video Website BibTeX
  • 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
    Accurate Mapping and Planning for Autonomous Racing
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2021
    arXiv IEEE Xplore Video BibTeX

Journal Papers

  • J. Arce, N. Vödisch, D. Cattaneo, W. Burgard, and A. Valada
    PADLoC: LiDAR-Based Deep Loop Closure Detection and Registration Using Panoptic Attention
    IEEE Robotics and Automation Letters (RA-L), vol. 8, issue 3, pp. 1319-1326, March 2023
    arXiv IEEE Xplore Video Website BibTex
  • N. Vödisch, O. Unal, K. Li, L. Van Gool, and D. Dai
    End-to-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization
    IEEE Robotics and Automation Letters (RA-L), vol. 7, issue 2, pp. 2242-2249, April 2022
    arXiv IEEE Xplore Video Code BibTeX
  • N. Vödisch*, D. Dodel*, and M. Schötz*
    FSOCO: The Formula Student Objects in Context Dataset
    SAE International Journal of Connected and Automated Vehicles, vol. 5, 2022
    arXiv SAE Website BibTeX
The asterisk (*) denotes equal contribution.
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