Your Job:
This thesis focuses on designing, evaluating, and deploying algorithms for robot perception and control.
The main task is predicting both self-motion and the motion of surrounding agents to enable safe navigation, using the event camera as the primary sensor.
The approach leverages prior information such as object detection, optical flow, and depth, while also integrating multimodal inputs including IMUs, radar, and standard cameras to ensure robust perception in complex environments.
Experiments are conducted using established outdoor benchmark datasets as well as a custom indoor dataset created for this work.
Data is collected in a laboratory with a robotic platform equipped with state-of-the-art sensors.
The project evaluates SNNs, RNNs, and Transformers to exploit the temporal resolution of event cameras, with the goal of achieving accuracy, efficiency, and real-time performance on robotic platforms powered by edge-computing hardware.
In addition, knowledge of Model Predictive Control (MPC) is considered valuable for integrating perception with control during deployment.
Your Profile:
Please feel free to apply for the position even if you do not have all the required skills and knowledge.
We may be able to teach you missing skills during your induction.
Our Offer:
We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with:
In addition to exciting tasks and a collegial working environment, we offer you much more:
Place of employment: Aachen
We welcome applications from people with diverse backgrounds, in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin.
A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.