Intern/Thesis - Learning-based Radar-Camera Fusion for Simultaneous Localization and Mapping (f/m/d)
Indexed description
Join us and be part of this exciting journey!
YOUR TEAM
You will join our ground truth generation team working at the intersection of academia and industry. Working closely with our PhD researchers, you will contribute to ongoing research on radar-camera fusion and robust simultaneous localization and mapping (SLAM) for autonomous vehicles. Together, we tackle challenging problems in radar-camera state estimation, robust localization, consistent map generation, and cross-modal alignment between vision and radar data. Our goal is to advance multi-modal SLAM algorithms that combine complementary sensing characteristics to enable reliable localization and mapping in complex driving environments under all weather and lighting conditions.
What You Will Do
- Independently investigating novel approaches for radar-camera fusion
- Developing and evaluating methods for robust state estimation, localization and/or 3D map generation
- Designing experiments and comparing your approach against state-of-the-art methods
- Analyzing results and deriving insights for multi-modal SLAM algorithms
- Excellent academic performance
- Enrolled student in Computer Science, Robotics, Electrical Engineering, Mathematics or equivalent
- Know-how of relevant sensors for autonomous driving and measurement technology
- Programming knowledge and experience in Python and/or C++
- Confident in English in both oral and written form
- Open-minded team player, passionate about self-driving technologies and solving hard problems
- First research experience (e.g., internships, projects, papers, or coding competitions) is a plus
- Remote work options within Germany
- Duration: 3 - 6 months
- 35 h/week
- Salary: 13,90 €/hour
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