Probabilistic Techniques for Mobile Robot Mapping and Exploration

Dr. Burgard is from the Univ. of Freiburg, Germany
In recent years, probabilistic techniques have enabled novel and
innovative solutions to some of the most important problems in mobile
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.