Probabilistic Techniques for Mobile Robot Navigation

Abstract

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 techniques currently being developed in my group regarding the problem of controlling a robot to efficiently learn a map of an unknown environment. I then will describe how a team of mobile robots can be coordinated to effectively explore unknown environments. Additionally, I will present probabilistic approaches to learn three-dimensional models from range data as well as techniques for classifying places based on range and vision data. 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.