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Automation Technology
Stefan Schubert

M.Sc. Stefan Schubert

Portrait: M.Sc. Stefan Schubert
M.Sc. Stefan Schubert
  • Phone:
    +49 371 531-33282
  • Email:
  • Address:
    Reichenhainer Straße 70, 09126 Chemnitz
  • Room:
    2/W125 (new: C25.125)

Research Interests

My research interests are within the field of mobile robotics and artificial intelligence. I am working on environment perception, navigation, and machine learning for decision making and object detection.

More information can be found here:

Awards

RSS Pioneers 2019 (RSSP'19) Attendees. Stefan Schubert

RSS Pioneer 2019 (Robotics: Science and Systems)

I was one of the 23 international PhD students and postdocs who have been accepted to this year's RSS Pioneers Workshop (RSSP'19). Thanks to the RSSP'19 general chairs Tesca Fitzgerald (Georgia Institute of Technology, USA) and Abhinav Valada (University of Freiburg, Germany) as well as all other organizers.

RSSP'19 Research Statement (pdf)

TAROS 2017 Best Paper Award

We presented the best paper during the Conference Towards Autonomous Robotic Systems (TAROS) held at the University of Surrey, UK. Special thanks to the conference chair Prof. Yang Gao and all other organizers.

Schubert, S., Neubert, P. & Protzel, P. (2017) Towards camera based navigation in 3D maps by synthesizing depth images. In Proc. of Towards Autonomous Robotic Systems (TAROS). DOI: 10.1007/978-3-319-64107-2_49. Best Paper Award Winner

TAROS 2016 Best Paper Award. How to Build and Customize a High-Resolution 3D Laserscanner Using Off-the-shelf Components. Stefan Schubert

TAROS 2016 Best Paper Award

We presented the best paper during the Conference Towards Autonomous Robotic Systems (TAROS) held at the University of Sheffield, UK. Special thanks to the conference chairs Prof. Tony Prescott and Prof. Jacques Penders and all other organizers.

Schubert, S., Neubert, P. & Protzel, P. (2016) How to Build and Customize a High-Resolution 3D Laserscanner Using Off-the-shelf Components. In Proc. of Towards Autonomous Robotic Systems (TAROS). DOI: 10.1007/978-3-319-40379-3_33. Best Paper Award Winner

Prize of the Dresden Circle of Business and Science. Stefan Schubert

Prize of the Dresden Circle of Business and Science, 2015

Award winner as junior scientist in the field of natural sciences, Dresden, Germany.

(left to right: Michael von Bronk (Dresden Circle of Business and Science), Prof. Dr. Michael Ruck (TU Dresden), Dr. Andreas Handschuh (TU Bergakademie Freiberg), Dr.-Ing. Stefan Schafföner (Prize Winner, TU Bergakademie Freiberg), M.Sc. Stefan Schubert (Prize Winner, TU Chemnitz), Prof. Dr. Peter Protzel (TU Chemnitz), Prof. Dr. Endrik Wilhelm (Dresden Circle of Business and Science))


German National Scholarship. Stefan Schubert

German National Scholarship, 2011-2013

Successful application for the German National Scholarship (merit-based scholarship).

Tutorials

ECAI 2020 logo. Stefan Schubert

An Introduction to Vector Symbolic Architectures and Hyperdimensional Computing, ECAI 2020

We held a tutorial (tutorial website) on high dimensional computing at the 2020 European Conference on Artificial Intelligence (ECAI). We gave an introduction to properties of the high dimensional space, Vector Symbolic Architectures (VSAs), high-dimensional encoding of real world data, and applications. More information about the topic can be found in our recent journal article:

Neubert, P., Schubert, S. & Protzel, P. (2019) An Introduction to Hyperdimensional Computing for Robotics. KI - Künstliche Intelligenz, Special Issue: Reintegrating Artificial Intelligence and Robotics, Vol. 33. DOI: 10.1007/s13218-019-00623-z
KI 2019 Tutorial on High dimensional computing - the upside of the curse of dimensionality. Stefan Schubert

High dimensional computing - the upside of the curse of dimensionality, KI 2019

We held a tutorial (tutorial website) on high dimensional computing at the 2019 German conference on artificial intelligence (KI) in Kassel, Germany. We gave an introduction to properties of the high dimensional space, Vector Symbolic Architectures (VSAs), high-dimensional encoding of real world data, and applications. More information about the topic can be found in our recent journal article:

Neubert, P., Schubert, S. & Protzel, P. (2019) An Introduction to Hyperdimensional Computing for Robotics. KI - Künstliche Intelligenz, Special Issue: Reintegrating Artificial Intelligence and Robotics, Vol. 33. DOI: 10.1007/s13218-019-00623-z

Publications

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: 10.1109/LRA.2021.3052446

Neubert, P. & Schubert, S. (2021) Hyperdimensional computing as a framework for systematic aggregation of image descriptors. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (to appear)

Schubert, S., Neubert, P. & Protzel, P. (2021) Beyond ANN: Exploiting Structural Knowledge for Efficient Place Recognition. In Proc. of Intl. Conf. on Robotics and Automation (ICRA). (to appear)

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. In Proc. of Intl. Conf. on Robotics and Automation (ICRA). (to appear)

Neubert, P., Schubert, S. & Protzel, P. (2021) Resolving Place Recognition Inconsistencies Using Intra-Set Similarities. IEEE Robotics and Automation Letters (RA-L) and ICRA. DOI: 10.1109/LRA.2021.3060729

Schubert, S., Neubert, P. & Protzel, P. (2020) Unsupervised Learning Methods for Visual Place Recognition in Discretely and Continuously Changing Environments. In Proc. of Intl. Conf. on Robotics and Automation (ICRA). DOI: 10.1109/ICRA40945.2020.9197044

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. IEEE Sensors Journal. DOI: 10.1109/JSEN.2020.2996757

Neubert, P., Schlegel, K., Schubert, S. & Protzel, P. (2020) Experiences and Open Questions on using Vector Symbolic Architectures for Mobile Robotics. In Workshop on Developments in Hyperdimensional Computing and Vector Symbolic Architectures. (to appear)

Neubert, P., Schubert, S. & Protzel, P. (2019) An Introduction to Hyperdimensional Computing for Robotics. KI - Künstliche Intelligenz, Special Issue: Reintegrating Artificial Intelligence and Robotics, Vol. 33. DOI: 10.1007/s13218-019-00623-z

Schubert, S., Neubert, P. & Protzel, P. (2019) Towards combining a neocortex model with entorhinal grid cells for mobile robot localization. In Proc. of European Conference on Mobile Robotics (ECMR). DOI: 10.1109/ECMR.2019.8870939

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) and presentation at Intl. Conf. on Intelligent Robots and Systems (IROS). DOI: 10.1109/LRA.2019.2927096

Circular Convolutional Neural Networks for Panoramic Images and Laser Data

Abstract: Circular Convolutional Neural Networks (CCNN) are an easy to use alternative to CNNs for input data with wrap-around structure like 360° images and multi-layer laserscans. Although circular convolutions have been used in neural networks before, a detailed description and analysis is still missing. This paper closes this gap by defining circular convolutional and circular transposed convolutional layers as the replacement of their linear counterparts, and by identifying pros and cons of applying CCNNs. We experimentally evaluate their properties using a circular MNIST classification and a Velodyne laserscanner segmentation dataset. For the latter, we replace the convolutional layers in two state-of-the-art networks with the proposed circular convolutional layers. Compared to the standard CNNs, the resulting CCNNs show improved recognition rates in image border areas. This is essential to prevent blind spots in the environmental perception. Further, we present and evaluate how weight transfer can be used to obtain a CCNN from an available, readily trained CNN. Compared to alternative approaches (e.g. input padding), our experiments show benefits of CCNNs and transfered CCNNs regarding simplicity of usage (once the layer implementations are available), performance and runtime for training and inference. Implementations for Keras with Tensorflow are provided online

Schubert, S., Neubert, P., Pöschmann, J. & Protzel, P. (2019) Circular Convolutional Neural Networks for Panoramic Images and Laser Data. In Proc. of Intelligent Vehicles Symposium (IV). DOI: 10.1109/IVS.2019.8813862

Neubert, P., Schubert, S. & Protzel, P. (2017) Sampling-based Methods for Visual Navigation in 3D Maps by Synthesizing Depth Images. In Proc. of Intl. Conf. on Intelligent Robots and Systems (IROS). DOI: 10.1109/IROS.2017.8206067

Pöschmann, J., Neubert, P., Schubert, S. & Protzel, P. (2017) Synthesized semantic views for mobile robot localization. In Proc. of European Conf. on Mobile Robotics (ECMR). DOI: 10.1109/ECMR.2017.8098662

Schubert, S., Neubert, P. & Protzel, P. (2017) Towards camera based navigation in 3D maps by synthesizing depth images. In Proc. of Towards Autonomous Robotic Systems (TAROS). DOI: 10.1007/978-3-319-64107-2_49. Best Paper Award Winner

Neubert, P., Schubert, S. & Protzel, P. (2016) Learning Vector Symbolic Architectures for Reactive Robot Behaviours. In Proc. of Intl. Conf. on Intelligent Robots and Systems (IROS) Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics

Schubert, S., Lange, S., Neubert, P. & Protzel, P. (2016) Map Enhancement with Track-Loss Data in Visual SLAM. In Proc. of Intl. Conf. on Intelligent Robots and Systems (IROS) Workshop on State Estimation and Terrain Perception for All Terrain Mobile Robots

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. In Proc. of Intl. Symposium on Robotics (ISR)

Schubert, S., Neubert, P. & Protzel, P. (2016) How to Build and Customize a High-Resolution 3D Laserscanner Using Off-the-shelf Components. In Proc. of Towards Autonomous Robotic Systems (TAROS). DOI: 10.1007/978-3-319-40379-3_33. Best Paper Award Winner

Neubert, P., Schubert, S. & Protzel, P. (2015) Exploiting intra Database Similarities for Selection of Place Recognition Candidates in Changing Environments. In Proc. of. Computer Vision and Pattern Recognition (CVPR) Workshop on Visual Place Recognition in Changing Environments