Master Thesis - Improving 3D LiDAR Localization of Mobile Robots with Point Cloud Segmentation
In the Professional Service Robots - Outdoor research group we develop autonomous, mobile robots for a variety of outdoor applications, such as agriculture and forestry. The focus is on the development of an autonomous outdoor navigation solution as well as the hardware of the robots. Natural outdoor environments like farms and forests are filled with highly complex geometrical forms and dynamically changing appearances compared to solidly constructed areas or indoor environments. One major challenge to enable autonomous operations in these environments is a reliable localization. Leveraging 3D LiDAR data for creating 3D point cloud maps and localizing in these maps by scan matching solves this problem for static environments. But with an increased presence of dynamic objects and large appearance changes due to weather and season these algorithms can lead to wrong associations and degraded performances. The classification and segmentation of the point cloud information can be a major assistance for the localization approach to improve the associations and scan matching algorithms.
What you will do
In this thesis you will contribute to the environment understanding and the localization of the robot by developing a point cloud segmentation method based on classical and/or deep learning techniques. You will integrate this segmentation method into the 3D LiDAR SLAM approach and evaluate localization improvements. One focus will lie on the separation of dynamic, static and semi-static objects into different classes and how to treat these classes in the SLAM approach. Based on a current state of research, you will evaluate existing approaches and derive a novel concept for segmentation in versatile outdoor environments. During the implementation with ROS in C++/Python you will regularly test and evaluate your algorithms on our outdoor robot platforms.
What you bring to the table
Background in Computer Science, Software Engineering, Electrical Engineering, Mechatronics or similar Enrolled student at a German university Profound knowledge of C++ /Python Experience with ROS Background in neural networks for image or point cloud processing Analytical mindset and experience in algorithm development Enthusiasm for mobile robotics Fluent in English or German
What you can expect
Familiar atmosphere including Feature Friday (cake + presentations or demos)
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.
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.
Interested? Apply online now. We look forward to getting to know you!
If you are interested, please upload a short letter of motivation, CV and current grade transcript in our job portal. If you have any questions about the application process, please contact: Jennifer Leppich jennifer.leppich@ipa.fraunhofer.de For questions about the content of the Master's thesis, please contact: Domink Moss Navigation mobile Roboter dominik.moss@ipa.fraunhofer.de
Recruiting
Tel. +49 711 970-1415
Fraunhofer Institute for Manufacturing Engineering and Automation IPA
Requisition Number: 78494 Application Deadline:
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