Probabilistic Techniques for Mobile Robot Navigation

Probabilistic approaches have been discovered as one of the most powerful approaches to highly relevant problems in mobile robotics including  perception and robot state estimation. Major challenges in the context of probabilistic
algorithms for mobile robot navigation lie in the questions of how to deal with highly complex state estimation problems and how to control the robot so  that it efficiently carries out its task. In this talk, I will present recently
developed techniques for efficiently learning a map of an unknown  environment with a mobile robot. I will also describe how this state estimation  problem can be solved more effectively by actively controlling the robot. For all
algorithms I will present experimental results that have been obtained with mobile  robots in real-world environments.