Stefan Schubert

Stefan Schubert

Research Fellow, Computer Vision, Visual Place Recognition, Visual Localization, Visual SLAM, Machine Learning & Robotics

Publications

see also Google Scholar

Theses

  1. Schubert, S. (2023). Visual Place Recognition in Changing Environments using Additional Data-Inherent Knowledge. PhD thesis. Chemnitz University of Technology. link
  2. Schubert, S. (2014). Optimierter Einsatz eines 3D-Laserscanners zur Point-Cloud-basierten Kartierung und Lokalisierung im In- und Outdoorbereich. Master’s thesis. Chemnitz University of Technology. link
  3. Schubert, S. (2012). Analyse und Regelung einer Mehrgrößenfüllstandsstrecke. Bachelor’s thesis. Chemnitz University of Technology.

Journal articles

  1. Yuan, F., Schubert, S., Protzel, P., Neubert, P. (2024). Local positional graphs and attentive local features for a data and runtime-efficient hierarchical place recognition pipeline. IEEE Robotics and Automation Letters (RA-L). doi, arxiv
  2. Schubert, S., Neubert, P., Garg, S., Milford, M., Fischer, T. (2023). Visual Place Recognition: A Tutorial. IEEE Robotics & Automation Magazine (RAM). doi. (Early Access). Open Access
  3. Schubert, S., Neubert, P., Protzel, P. (2021). Graph-Based Non-Linear Least Squares Optimization for Visual Place Recognition in Changing Environments. In IEEE Robotics and Automation Letters (RA-L). doi, arxiv
  4. Neubert, P., Schubert, S., Protzel, P. (2021). Resolving Place Recognition Inconsistencies using Intra-Set Similarities. In IEEE Robotics and Automation Letters (RA-L). doi
  5. Sudharshan, V., Seidel, P., Ghamisi, P., Lorenz, S., Fuchs, M., Fareedh, J. S., Neubert, P., Schubert, S., Gloaguen, R. (2020). Object detection routine for material streams combining RGB and hyperspectral reflectance databased on Guided Object Localization. In IEEE Sensors Journal. doi
  6. Neubert, P., Schubert, S., Protzel, P. (2019). An Introduction to High Dimensional Computing for Robotics. In KI – Künstliche Intelligenz, Special Issue: Reintegrating Artificial Intelligence and Robotics. doi, read online
  7. Neubert, P., Schubert, S., Protzel, P. (2019). A neurologically inspired sequence processing model for mobile robot place recognition. In IEEE Robotics and Automation Letters (RA-L). doi, pdf

Conference papers

  1. Neubert, P., Schubert, S. (2022). SEER: Unsupervised and sample-efficient environment specialization of image descriptors. Proc. of Robotics: Science and Systems (RSS). doi
  2. Schubert, S., Neubert, P., Protzel, P. (2021). Fast and Memory Efficient Graph Optimization via ICM for Visual Place Recognition. Proc. of Robotics: Science and Systems (RSS). doi
  3. Neubert, P., Schubert, S., Schlegel, K., Protzel, P. (2021). Vector Semantic Representations as Descriptors for Visual Place Recognition. Proc. of Robotics: Science and Systems (RSS). doi
  4. Neubert, P., Schubert, S. (2021). Hyperdimensional Computing as a Framework for Systematic Aggregation of Image Descriptors. Proc. of Conference on Computer Vision and Pattern Recognition (CVPR). doi, pdf
  5. Schubert, S., Neubert, P., Protzel, P. (2021). Beyond ANN: Exploiting Structural Knowledge for Efficient Place Recognition. Proc. of International Conference on Robotics and Automation (ICRA). doi, arxiv
  6. Yuan, F., Neubert, P., Schubert, S., Protzel, P. (2021). SoftMP: Attentive feature pooling for joint local feature detection and description for place recognition in changing environments. Proc. of International Conference on Robotics and Automation (ICRA). doi
  7. Schubert, S., Neubert, P., Protzel, P. (2020). Unsupervised Learning Methods for Visual Place Recognition in Discretely and Continuously Changing Environments. Proc. of International Conference on Robotics and Automation (ICRA). doi, arxiv
  8. Schubert, S., Neubert, P., Protzel, P. (2019). Towards combining a neocortex model with entorhinal grid cells for mobile robot localization. Proc. of European Conference on Mobile Robotics (ECMR). doi, pdf
  9. Schubert, S., Neubert, P., Pöschmann, J., Protzel, P. (2019). Circular Convolutional Neural Networks for Panoramic Images and Laser Data. Proc. of Intelligent Vehicles Symposium (IV). doi, pdf
  10. Neubert, P., Schubert, S., Protzel, P. (2017). Sampling-based Methods for Visual Navigation in 3D Maps by Synthesizing Depth Images. Proc. of International Conference on Intelligent Robots and Systems (IROS). doi, pdf
  11. Schubert, S., Neubert, P, Protzel, P. (2017). Towards camera based navigation in 3D maps by synthesizing depth images. Proc. of Towards Autonomous Robotic Systems (TAROS). doi, pdf. Best Paper Award Winner
  12. Pöschmann, J., Neubert, P., Schubert, S., Protzel, P. (2017). Synthesized semantic views for mobile robot localization. Proc. of European Conference on Mobile Robotics (ECMR). doi, pdf
  13. Schubert, S., Neubert, P., Protzel, P. (2016). How to Build and Customize a High-Resolution 3D Laserscanner Using Off-the-shelf Components. Proc. of Towards Autonomous Robotic Systems (TAROS). doi, pdf. Best Paper Award Winner
  14. Lange, S., Wunschel, D., Schubert, S., Pfeifer, T., Weissig, P., Uhlig, A., Truschzinski, M., Protzel, P. (2016). Two Autonomous Robots for the DLR SpaceBot Cup – Lessons Learned from 60 Minutes on the Moon. Proc. of International Symposium on Robotics (ISR). IEEE, pdf

Workshop papers

  1. Neubert, P., Schlegel, K., Schubert, S., Protzel, P. (2020). Experiences and Open Questions on using Vector Symbolic Architectures for Mobile Robotics. Proc. of Workshop on Developments in Hyperdimensional Computing and Vector Symbolic Architectures. pdf
  2. Schubert, S., Lange, S., Neubert, P., Protzel, P. (2016). Map Enhancement with Track-Loss Data in Visual SLAM. Proc. of International Conference on Intelligent Robots and Systems (IROS) Workshop on State Estimation and Terrain Perception for All Terrain Mobile Robots. pdf
  3. Neubert, P., Schubert, S., Protzel, P. (2016). Learning Vector Symbolic Architectures for Reactive Robot Behaviours. Proc. of International Conference on Intelligent Robots and Systems (IROS) Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics. pdf
  4. Neubert, P., Schubert, S., Protzel, P. (2015). Exploiting intra Database Similarities for Selection of Place Recognition Candidates in Changing Environments. Proc. of. Computer Vision and Pattern Recognition (CVPR) Workshop on Visual Place Recognition in Changing Environments. pdf

Preprints

  1. Schubert, S., Neubert, P. (2021). What makes visual place recognition easy or hard? arXiv. arxiv