Master Thesis - Multi-Sensor Motion Estimation for Autonomous Vehicles
Join us to design cutting-edge algorithms for multi-sensor motion estimation in self-driving cars. Get experience in computer vision and collaborate with industry experts on real-world challenges!
Why This Matters
Accurately estimating sensor motion, such as cameras and LiDARs, relative to the static environment is essential for tasks like object detection and tracking in self-driving cars. Currently, this task is performed for each sensor individually using methods like Visual-Inertial Odometry (VIO) or Simultaneous Localization and Mapping (SLAM). We believe that optimizing vehicle motion and sensor calibration for multiple sensors simultaneously could enhance accuracy and speed.
In this exciting project, you’ll have the opportunity to work with a variety of sensors, including forward and rear-facing cameras, fisheye cameras for parking, long-range LiDARs, and potentially radars. You'll explore new methods to improve self-driving car performance by working at the intersection of motion estimation and sensor calibration.
Your Role in the Project
As part of this master’s thesis project, you will:
- Design and implement an algorithm for multi-sensor motion estimation and calibration.
- Evaluate your algorithm against existing single-sensor solutions.
- Contribute to advancing our product by improving the speed and accuracy of motion estimation and sensor calibration.
What We're Looking For
We’re looking for motivated students who have:
- A strong foundation in mathematics (calculus, optimization, statistics).
- Proficiency in software development and debugging, particularly in C++ (for algorithm development) and Python (for evaluation frameworks).
- Familiarity with computer vision concepts (e.g., rigid body transformations, projective camera models).
What’s in it for You?
- Hands-on experience: Engage with cutting-edge autonomous driving technologies and real-world challenges.
- Mentorship & support: Collaborate with industry experts and work alongside a dynamic, innovative team.
- Skill development: Deepen your knowledge of computer vision, algorithm development, and sensor fusion.
- Career growth: Build a strong foundation for future opportunities in advanced technologies while expanding your professional network.
How to Apply & Important Details
We encourage two-person teams for this project, though individual applicants are also welcome. If applying with a partner, each applicant should submit an individual application, but mention who you plan to work with.
- Planned start: January 2025 (with flexibility).
- Final application date: November 15, 2024 (applications will be reviewed on an ongoing basis).
- Duration: 30 ECTS.
For further questions or more details, please contact Erik Stenborg at erik.stenborg@zenseact.com.
More about Zenseact
Our software makes a difference.
Using AI-based technology to create the ultimate driver support, we’re fighting to end car accidents and make roads safe for everyone. Around 1,4 million people die in traffic yearly while approximately 50 million people get injured. Many get disabled as a result of their injury. We can do better.
One purpose, one product.
We’re a software company dedicated to revolutionizing car safety. By designing the complete software stack for autonomous driving and advanced driver-assistance systems, we’re fighting to end car accidents and make roads safe for everyone. Zenseact was founded by Volvo Cars, and the teams are based in Gothenburg, Sweden, and Shanghai, China.When we aim for zero accidents faster, we strive to speed up the transition to safe automation. This is essentially achieved by making cars updatable – like a computer or a phone. With regular software updates, a vehicle can be made safer long after its production. By accelerating improvement loops, shortening development cycles, and deploying high-capacity software quickly, we can make cars safer, faster.
Culture with people at heart
To achieve our mission of saving lives and ending traffic accidents is to go where nobody has before. It requires us to venture into the unknown, pioneering new technology and pushing the frontier of autonomous driving. While there’s no denying our determination and expertise, we must stand united to succeed. By fostering a culture of support and enablement – a place of psychological safety where all of us can thrive – everything else will follow. We call this a people-at-heart culture. This culture means caring. It means the company cares about me, and we care about one another. It means sharing, so we give each other energy and have fun together. Our culture is also about belonging. It’s important to feel at home and that we can be ourselves at work. Finally, a people-at-heart culture means well-being. So, we enjoy the flexibility needed to be and do our best – at work and in life.
Zenseact works proactively to create a culture of diversity and inclusion, where individual differences are appreciated and respected. To drive innovation we see diversity as an asset, which means we value and respect differences in gender, race, ethnicity, religion or other belief, disability, sexual orientation or age etc.
Interviews are held on a continuous basis, so we highly recommend that you submit your application at your earliest convenience.
- Competence area
- Opportunities for Students, Graduates & Innovators
- Locations
- Gothenburg, Sweden
- Remote status
- Hybrid
Gothenburg, Sweden
About Zenseact
One purpose, one product
We are a software company focused on transforming car safety. By developing a complete software stack for autonomous driving and advanced driver-assistance systems, we aim to eliminate car accidents and make roads safer for all. Founded by Volvo Cars, Zenseact operates globally, with teams in Gothenburg and Lund, Sweden; Munich, Germany; and Shanghai, China.
Master Thesis - Multi-Sensor Motion Estimation for Autonomous Vehicles
Join us to design cutting-edge algorithms for multi-sensor motion estimation in self-driving cars. Get experience in computer vision and collaborate with industry experts on real-world challenges!
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