Probabilistic state estimation techniques for autonomous and decision support systems

Abstract

One of the ultimate goals of the field of artificial intelligence and robotics is to develop systems that assist us in our everyday lives by autonomously carrying out a variety of different tasks. To achieve this and to generate appropriate actions, such systems need to be able to accurately interpret their sensory input and estimate their state or the state of the environment to be successful. In recent years, probabilistic approaches have emerged as a key technology for these problems. In this article, we will describe state-of-the-art solutions to challenging tasks from the area of mobile robotics, autonomous cars, and activity recognition, which are all based on the paradigm of probabilistic state estimation. © 2011 Springer-Verlag.