Probabilistic Techniques for Mobile Robot Navigation
Prof. Wolfram Burgard (University of Freiburg, Germany)
Oct 28, 2011 11:00 AM
Probabilistic approaches have been discovered as one of the most powerful approaches to highly relevant problems in mobile robotics including robot state estimation and localization. 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 discuss both aspects and present recently developed techniques for efficiently learning a map of an unknown environment with a mobile robot using particle filters. I will also describe how the complexity of this state estimation problem can be reduced by actively controlling the vehicle. For all algorithms I will present experimental results that have been obtained with mobile robots in real-world environments. I will conclude the presentation with a discussion of open issues and potential directions for future research.
This page is
maintained by Yumi Yi (email@example.com).