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Prof. 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 Prof. Burgard and his group have 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.In his previous position from 1996 to 1999 at the University of Bonn Prof. Burgard was head of the research lab for Autonomous Mobile Systems. In 1997 they deployed Rhino as the first interactive mobile tour-guide robot in the Deutsches Museum Bonn in Germany (see corresponding overview article). In 1998 Prof. Burgard and his group went to Washington, DC, to install the mobile robot Minerva in the Smithsonian Museum of American History. Afterwards they produced several robots that autonomously operated in trade shows and Museums. In 2008, they developed an approach that allowed a car to autonomously navigate through a complex parking garage and park itself. In 2012, they developed the robot Obelix that autonomously navigated like a pedestrian from the campus of the Faculty of Engineering to the city center of Freiburg. Prof. Burgard have published over 250 papers and articles in robotic and artificial intelligence conferences and journals. In 2005, Prof. Burgard co-authored two books. Whereas the first one, entitled Principles of Robot Motion - Theory, Algorithms, and Implementations, is about sensor-based planning, stochastic planning, localization, mapping, and motion planning, the second one, entitled Probabilistic Robotics, covers robot perception and control in the face of uncertainty.
Autonomous robots are faced with a series of state estimation and learning problems to optimize their behavior. In this presentation I will start from probabilistic approaches and then describe recent methods developed in my group based on deep learning architectures for different perception problems including object recognition and segmentation and using RGB(-D) images. In addition, I will present a terrain classification approaches that utilize sound and vision. Finally, I will discuss the applicability of deep learning methods to robot navigation. For all approaches I will describe extensive experiments quantifying in which way the corresponding approaches extend the state of the art. This talk is designed to stimulate the discussion about potential directions in robotics and if they should be model- or data-driven.