Research seminar

Research seminar

Abstract

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.

Speaker biography

Wolfram Burgard is a professor for computer science at the University of Freiburg and head of the research lab for Autonomous Intelligent Systems. His areas of interest lie in artificial intelligence and mobile robots. His research mainly focuses on the development of robust and adaptive techniques for state estimation and control. Over the past years his group developed a series of innovative probabilistic techniques for robot navigation and control. They cover different aspects such as localization, map-building, SLAM, path-planning, exploration, and several other aspects.