Suitable datasets are an integral part of robotics research, especially for training neural networks in robot perception. However, in many domains, suitable real-world data are scarce and cannot be easily obtained. This problem is especially prevalent for unstructured outdoor environments, in particular, planetary ones. Recent advances in photorealistic simulations help researchers to simulate close-to-real data in many domains. Synthetic planetary data requires careful modeling and annotation of many different terrain aspect and details, such as textures and distributions of rocks, to become a valuable test-bed for robotics. To fill this gap, DLR developed OAISYS, a simulator specifically designed for the needs of planetary robotics visual tasks, but also applicable for other outdoor environments. The simulator is capable of generating large varieties of (planetary) outdoor scenes with rich generation of meta data, such as multi-level semantic and instance annotations.
We are seeking to further improve our simulator in many aspects and need your help in doing so. You will help to develop new modules for the simulator, which will be used for current and future robotic missions. Furthermore, you will contribute to the further development of the core code of the simulator.
This is an excellent opportunity to work on a cutting-edge topic, together with a great team, to get creative in simulation and see your work applied to real robots.
- literature search on possible new methods and techniques for the simulator
- develop new modules for OAISYS
- develop on core software architecture of OAISYS
Read more about the student job here https://www.dlr.de/dlr/jobs/en/desktopdefault.aspx/tabid-10596/1003_read-47555/