Master Thesis - Out-of-distribution detection + annotation for traversability estimation for robots

Stellenbeschreibung:

Advertisement for the field of study such as: Automation technology, electrical engineering, computer science, cybernetics, mechanical engineering, mathematics, mechatronics, control engineering, software design, software engineering, technical computer science or comparable.

In the Professional Service Robots - Outdoor research group we develop autonomous, mobile robots for a variety of outdoor applications, such as agriculture, forestry and logistics. The focus is on the development of an autonomous outdoor navigation solution as well as the hardware of the robots.

For mobile robots operating in outdoor, unstructured environments with unknown terrain conditions, accurately understanding the traversability of the surrounding environment is essential. This ensures that the robot navigates through safe paths, avoiding difficult terrains such as mud and dense vegetation and preventing collision with obstacles.

A key approach in this research field consists of using Deep Neural Networks and Foundation Models to perform traversability inference on incoming camera images. However, a major challenge lies in the lack of extensive datasets for the different field environments and the high effort in acquiring and labeling the data set. This is especially limiting for robots operating in a wide variety of environments and performing exploration tasks.

Therefore, state-of-the-art semantic traversability classification algorithms need to be extended with the capability to detect out-of-distribution environments and to autonomously infer their traversability.

Be part of change

In this thesis, you will design a semantic traversability classification DNN pipeline capable of performing long-term traversability estimation. You will focus on extending our state-of-the-art few-shot segmentation DNN algorithm to enable automatic adaptation to new environments. In particular, you will evaluate different methods to identify out-of-distribution domains. You will also evaluate different approaches for automated model-learning using the robot’s experience in these new environments. You will test your implementation in real-world scenarios using both recorded data and real-life deployment in our mobile CURT robots.

What you contribute

Student enrolled at a German university/Hochschule
Background in Computer Science, Software Engineering, Mechatronics or similar
Experience with deep learning frameworks such as Keras / TensorFlow / PyTorch
Experience in developing and testing deep learning models for computer vision applications is beneficial.
Analytical mindset
Enthusiasm for mobile robotics
Fluent in English or German

What we offer

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

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. Our tasks are diverse and adaptable – for applicants with disabilities, we work together to find solutions that best promote their abilities. The same applies if they do not meet all the profile requirements due to a disability.

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.