City:  Stuttgart
Date:  Apr 11, 2024

Master Thesis - Cross-domain Semantic Segmentation for Unstructured Outdoor Environments

The Fraunhofer-Gesellschaft ( currently operates 76 institutes and research institutions throughout Germany and is the world’s leading applied research organization. Around 30 800 employees work with an annual research budget of 3.0 billion euros. 

In the Professional Service Robots - Outdoor research group we develop autonomous mobile robots for a variety of outdoor applications, such as agriculture and forestry. The focus is on the development of an autonomous outdoor navigation solution as well as the hardware of the robots.
Environment perception is a key component for the interpretation of the surroundings of autonomous robots in outdoor areas. Camera-based solutions have been developed as an approach to visual perception. To this end, semantic segmentation has been researched and improved over the years to effectively understand the scene so that robots can reliably navigate their environment. However, semantic segmentation also brings with it a number of challenges. One major challenge is robust generalisation across multiple domains when trained on a specific source domain.


What you will do

The aim of this master thesis is therefore to explore the approaches of domain adaptation or domain generalisation and to develop a robust segmentation approach that generalises well across multiple similar domains and not just the source domain on which it was trained. This helps to reduce or possibly even eliminate the need to annotate datasets for each new target domain encountered by the robot. The experiments are conducted in the context of unstructured outdoor environments characterised by uneven terrain and objects and obstacles of different shapes and sizes. 


Your tasks include the implementation of robust models for environment interpretation as well as experiments with domain adaption and/or domain generalization for semantic segmentation.


What you bring to the table

  • Background in Computer Science, Software Engineering, Electrical Engineering, Mechatronics or similar 
  • Profound knowledge of C/C++ (Experience with ROS is a plus)
  • Profound knowledge in deep learning 
  • Experience in deep learning frameworks such as Keras/Tensorflow/PyTorch 
  • Enthusiasm for mobile robotics
  • Fluent in English or German 


What you can expect

  • Cutting-edge technology in the field of outdoor mobile robotics 
  • Hands on with our robots on our own test fields in Stuttgart
  • Take on responsibility and freedom to implement your own ideas
  • Work with the best students in their discipline 
  • Familiar atmosphere including Cake Friday


We also offer the possibility of direct entry for excellent graduates.


We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. 

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future. 

If you are interested, please submit a short letter of motivation on your personal favourite topic listed above, CV, current grade transcript to 

Fraunhofer Institute for Manufacturing Engineering and Automation IPA 


Requisition Number: 71223                Application Deadline:


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