 Autonomous Systems LabOpen OpportunitiesOne of the new disciplines at the upcoming CYBATHLON is the vision assistance race. Visual impaired people are severely limited in their autonomy of completing many daily tasks. Available vision aids are limited to specific domains, such as reading text out loud, but fail to generalize. Smart vision assistive technologies could provide more intuitive, comprehensive and reliable support in daily tasks.
The CYBATHLON challenges contain a variety of daily situations, such as shopping, finding a free seat or ringing the correct doorbell. The goal is to develop an assistive device capable of fulfilling all challenges.
- Computer Vision, Image Processing, Intelligent Robotics, Mechanical Engineering, Neural Networks, Genetic Alogrithms and Fuzzy Logic
- Semester Project
| The project aims to implement a semantic label transfer from satellite to aerial imagery in order to enable the training of image-based machine learning algorithms for autonomous aerial vehicle tasks, such as path planning, collision avoidance, and localization. - Computer Vision, Intelligent Robotics
- Semester Project
| Bundle Adjustment (BA) is a critical optimization technique used to refine a visual reconstruction by jointly estimating the 3D scene structure and the viewing parameters. Traditional BA approaches primarily focus on geometric features and might struggle in highly unstructured scenarios, such as natural environments.
This project aims to extend the Bundle Adjustment methodology by incorporating higher-level features extracted from semantic segmentation. The integration of semantic information aims to provide contextually relevant and more discriminative data to the adjustment process, thereby improving its accuracy and robustness.
- Computer Vision, Image Processing
- Master Thesis, Semester Project
| Natural environments are inherently dynamic and heterogeneous, characterized by significant variability in appearance and structure. The primary goal of this project is to develop a training scheme capable of adapting lightweight semantic segmentation models to changing environments without relying on manually annotated data. In particular, the student will investigate advanced domain adaptation techniques to bridge the gap between synthetic and real data, as well as between sets of real images collected during different seasons. The developed approach is expected to enhance the model’s generalization capabilities, allowing it to adapt more rapidly to unseen conditions, and ultimately improving the robots' operational efficacy and robustness. - Computer Vision, Image Processing
- Master Thesis, Semester Project
| In this project, the student will explore efficient ways of modeling a natural environment to support long-term robotic navigation. - Computer Vision, Intelligent Robotics
- Master Thesis, Semester Project
| Digital environments, or digital twins, allow for design, prototyping, and testing in the virtual world before moving to the real world, thus accelerating development and reducing costs. A digital twin of a farm supports crop operations such as scheduling a harvest or predicting a yield, while agritech companies can develop farm automation robots using a digital twin. The goal of this project is to develop 3D Reconstruction and localization strategies that are capable to identify temporal invariant areas and properties in crop environments during the production season. The main target is to be able to match the same plants over time. - Computer Vision, Intelligent Robotics
- Master Thesis, Semester Project
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