Master Thesis: Class-aware Pointcloud Filtering and Matching for Long-Term SLAM

Call for applications for the field of study, such as: Computer Science, Software Engineering, Mechanical Engineering, Mechatronics or similar

In the research group Navigation Mobile Robots, we develop autonomous 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.

Be part of change

Since these outdoor applications have constantly changing environments due to weather conditions, seasonal variations, new constructions, and dynamic obstacles, the robot must continuously update its reference map of the whole environment. The localization module must understand which parts of the environment are static and which are semi-static or dynamic and update the global pointcloud reference map accordingly. Furthermore, the scan matching of the live pointclouds with this sparser global pointcloud map must be improved to get good pose estimate even with many dynamic obstacles blocking the view.

You will develop a global pointcloud mapping pipeline consisting of a pointcloud filtering approach and a pointcloud matching algorithm. The filtering approach should utilise camera-based classifications, from a sperate module, in the pointcloud data to distinguish different object types and construct and maintain a consistent global pointcloud map. The scan matching algorithm needs to be suitable for sparse environments with dynamic obstacles and must result in very few false positive matches. There already exists a basis of a 3D-LiDAR Localization module, that uses a local pointcloud map for odometry and a static global pointcloud map for absolute reference and your algorithm will replace the static global pointcloud map with your new updating global pointcloud mapping pipeline.
You will implement your solution inside our ROS2, C++ navigation stack and evaluate its performance both in simulation and in different real-world scenarios with our mobile CURT robots.

What you contribute

• Valid enrollment at a German university or college
• Programming knowledge and experience
• Experience with ROS is a plus
• 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

Remuneration dependent on qualifications.

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. Remuneration according to the general works agreement for employing assistant staff.

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.

Ready for a change? Then apply now and make a difference! Once we have received your online application, you will receive an automatic confirmation of receipt. We will then get back to you as soon as possible and let you know what happens next.

Ms. Jennifer Leppich

Recruiting

+49 711 970-1415

[email protected]

Fraunhofer Institute for Manufacturing Engineering and Automation IPA

Requisition Number: 85224