Daniel Wilbers (Graduated 2021)

Ph.D. Student

Contact:
Email: daniel.wilbers@nullgooglemail.com
Tel: +49 – 228 – 73 – 27 13
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, EG, room 0.013

Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Research Interests

• Autonomous Cars
• SLAM and localization

Publications

2019

• D. Wilbers, C. Merfels, and C. Stachniss, “Localization with Sliding Window Factor Graphs on Third-Party Maps for Automated Driving,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2019.
  [BibTeX] [PDF]

  @InProceedings{wilbers2019icra,
  author = {D. Wilbers and Ch. Merfels and C. Stachniss},
  title = {{Localization with Sliding Window Factor Graphs on Third-Party Maps for Automated Driving}},
  booktitle = icra,
  year = 2019,
  }

• D. Wilbers, L. Rumberg, and C. Stachniss, “Approximating Marginalization with Sparse Global Priors for Sliding Window SLAM-Graphs,” in Proc. of the ieee intl. conf. on robotic computing (irc), 2019.
  [BibTeX] [PDF]
  

  Most autonomous vehicles rely on some kind of map for localization or navigation. Outdated maps however are a risk to the performance of any map-based localization system applied in autonomous vehicles. It is necessary to update the used maps to ensure stable and long-term operation. We address the problem of computing landmark updates live in the vehicle, which requires efficient use of the computational resources. In particular, we employ a graph-based sliding window approach for simultaneous localization and incremental map refinement. We propose a novel method that approximates sliding window marginalization without inducing fill-in. Our method maintains the exact same sparsity pattern as without performing marginalization, but simultaneously improves the landmark estimates. The main novelty of this work is the derivation of sparse global priors that approximate dense marginalization. In comparison to state-of-the-art work, our approach utilizes global instead of local linearization points, but still minimizes linearization errors. We first approximate marginalization via Kullback-Leibler divergence and then recalculate the mean to compensate linearization errors. We evaluate our approach on simulated and real data from a prototype vehicle and compare our approach to state-of-the-art sliding window marginalization.

  @InProceedings{wilbers2019irc-amws,
  author = {D. Wilbers and L. Rumberg and C. Stachniss},
  title = {{Approximating Marginalization with Sparse Global Priors for Sliding Window SLAM-Graphs}},
  booktitle = {Proc. of the IEEE Intl. Conf. on Robotic Computing (IRC)},
  year = 2019,
  abstract = {Most autonomous vehicles rely on some kind of map for localization or navigation. Outdated maps however are a risk to the performance of any map-based localization system applied in autonomous vehicles. It is necessary to update the used maps to ensure stable and long-term operation. We address the problem of computing landmark updates live in the vehicle, which requires efficient use of the computational resources. In particular, we employ a graph-based sliding window approach for simultaneous localization and incremental map refinement. We propose a novel method that approximates sliding window marginalization without inducing fill-in. Our method maintains the exact same sparsity pattern as without performing marginalization, but simultaneously improves the landmark estimates. The main novelty of this work is the derivation of sparse global priors that approximate dense marginalization. In comparison to state-of-the-art work, our approach utilizes global instead of local linearization points, but still minimizes linearization errors. We first approximate marginalization via Kullback-Leibler divergence and then recalculate the mean to compensate linearization errors. We evaluate our approach on simulated and real data from a prototype vehicle and compare our approach to state-of-the-art sliding window marginalization.},
  }

• D. Wilbers, C. Merfels, and C. Stachniss, “A Comparison of Particle Filter and Graph-based Optimization for Localization with Landmarks in Automated Vehicles,” in Proc. of the ieee intl. conf. on robotic computing (irc), 2019.
  [BibTeX] [PDF]

  @InProceedings{wilbers2019irc-cpfg,
  author = {D. Wilbers and Ch. Merfels and C. Stachniss},
  title = {{A Comparison of Particle Filter and Graph-based Optimization for Localization with Landmarks in Automated Vehicles}},
  booktitle = {Proc. of the IEEE Intl. Conf. on Robotic Computing (IRC)},
  year = 2019,
  }