Master Thesis - Development of a Visual SLAM System for Challenging Road Topographies (f/m/d)
Master Thesis - Development of a Visual SLAM System for Challenging Road Topographies (f/m/d)
Mönsheim, DE, 71297
Master Thesis - Development of a Visual SLAM System for Challenging Road Topographies (f/m/d)
We are CARIAD, the automotive software company of the Volkswagen Group. Our teams build automotive software platforms and digital customer functions for iconic brands like Audi, Volkswagen, and Porsche – supporting the Volkswagen Group in becoming the leading automotive technology company. With CARIDIANS in Germany, the USA, China, Estonia, and India, we are transforming automotive mobility for everyone.
Join us and be part of this exciting journey!
YOUR TEAM
Our “Chassis” team is excited to offer a Master Thesis opportunity focused on developing a monocular visual SLAM system that remains robust under challenging road topographies. Unlike conventional SLAM methods that often assume flat, uniform roads, your work will target real-world edge cases such as steep slopes, crests, cobblestones, gravel, and off-road conditions.
This project will involve benchmarking state-of-the-art monocular SLAM algorithms, designing improvements for robustness, and validating them on a unique multi-city driving dataset. You will also gain hands-on experience by evaluating your system in real-world driving scenarios.
Research question: How can monocular SLAM be made robust to diverse and non-flat road topographies in real-world driving?
WHAT YOU WILL DO
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Conduct a literature review on state-of-the-art monocular visual SLAM methods
-
Benchmark selected algorithms on diverse road topographies and identify failure modes
-
Develop and implement new methods to improve SLAM robustness under difficult conditions (e.g., steep slopes, low texture, dynamic traffic)
-
Document and present the results in your Master Thesis
-
Prepare results for publication in a scientific venue
WHO YOU ARE
-
Enrolled student in Computer Science, Mechatronics, Electrical Engineering, Robotics, or comparable field
-
Knowledge in the field of Computer Vision, and its libraries like OpenCV and NumPy
-
Programming skills in Python, previous experience with Tensorflow and/or PyTorch
-
Familiarity with SLAM, structure-from-motion, or visual odometry is a strong plus
-
Interest in optimization, geometry, and 3D perception
-
Team-minded, growth-oriented, communicative, and eager to tackle challenging real-world problems
NICE TO KNOW
-
Remote work options within Germany
-
Duration: 6 months
-
35 h/week
At CARIAD, we embrace individuality and diversity because we believe our differences make us stronger. We actively seek to build teams with a variety of backgrounds, perspectives, and experiences. Our goal is to create an environment where everyone feels valued and empowered to contribute. If you need assistance with your application due to a disability, please reach out to us at careers@cariad.technology - we are happy to support you.
We are CARIAD, the automotive software company of the Volkswagen Group. Our teams build automotive software platforms and digital customer functions for iconic brands like Audi, Volkswagen, and Porsche – supporting the Volkswagen Group in becoming the leading automotive technology company. With CARIDIANS in Germany, the USA, China, Estonia, and India, we are transforming automotive mobility for everyone.
Join us and be part of this exciting journey!
YOUR TEAM
Our “Chassis” team is excited to offer a Master Thesis opportunity focused on developing a monocular visual SLAM system that remains robust under challenging road topographies. Unlike conventional SLAM methods that often assume flat, uniform roads, your work will target real-world edge cases such as steep slopes, crests, cobblestones, gravel, and off-road conditions.
This project will involve benchmarking state-of-the-art monocular SLAM algorithms, designing improvements for robustness, and validating them on a unique multi-city driving dataset. You will also gain hands-on experience by evaluating your system in real-world driving scenarios.
Research question: How can monocular SLAM be made robust to diverse and non-flat road topographies in real-world driving?
WHAT YOU WILL DO
-
Conduct a literature review on state-of-the-art monocular visual SLAM methods
-
Benchmark selected algorithms on diverse road topographies and identify failure modes
-
Develop and implement new methods to improve SLAM robustness under difficult conditions (e.g., steep slopes, low texture, dynamic traffic)
-
Document and present the results in your Master Thesis
-
Prepare results for publication in a scientific venue
WHO YOU ARE
-
Enrolled student in Computer Science, Mechatronics, Electrical Engineering, Robotics, or comparable field
-
Knowledge in the field of Computer Vision, and its libraries like OpenCV and NumPy
-
Programming skills in Python, previous experience with Tensorflow and/or PyTorch
-
Familiarity with SLAM, structure-from-motion, or visual odometry is a strong plus
-
Interest in optimization, geometry, and 3D perception
-
Team-minded, growth-oriented, communicative, and eager to tackle challenging real-world problems
NICE TO KNOW
-
Remote work options within Germany
-
Duration: 6 months
-
35 h/week
At CARIAD, we embrace individuality and diversity because we believe our differences make us stronger. We actively seek to build teams with a variety of backgrounds, perspectives, and experiences. Our goal is to create an environment where everyone feels valued and empowered to contribute. If you need assistance with your application due to a disability, please reach out to us at careers@cariad.technology - we are happy to support you.
Mönsheim, DE, 71297