Probabilistic Techniques for Mobile Robot Mapping and Exploration

robotics. 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 some techniques currently being developed in my group regarding the problem of autonomously learning a
map of an unknown environment with a mobile robot. I will first present an efficient approach based on particle filters to solve the
simultaneous mapping and localization problem. Then I will describe how this approach can be combined with an exploration strategy that
simultaneously takes into account the uncertainty in the pose of the robot and in the map. For all algorithms I will present experimental
results, which have been obtained with mobile robots in real-world environments as well as in simulation. I will conclude the
presentation with a discussion of open issues and potential directions for future research.