Speaker: Prof. Dr. Wolfram Burgard (University of Technology Nuremberg)
For autonomous robots and automated driving, the capability to robustly perceive environments and execute their actions is the ultimate goal. The key challenge is that no sensors and actuators are perfect, which means that robots and cars need the ability to properly deal with the resulting uncertainty. In this presentation, Professor Wolfram Burgard will introduce the probabilistic approach to robotics, which provides a rigorous statistical methodology to deal with state estimation problems. Furthermore it will be discussed how this approach can be combined using state-of-the-art technology from machine learning to deal with complex and changing real-world environments.
Following the lecture there will be a reception.