Probabilistic and Deep Learning Techniques for Robot Navigation and Automated Driving - Prof. Burgard
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, I will introduce the probabilistic approach to robotics, which provides a rigorous statistical methodology to deal with state estimation problems. I will furthermore discuss how this approach can be extended using state-of-the-art technology from machine learning to deal with complex and changing real-world environments.
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RWTH Center for Artificial Intelligence