In recent years, particle filters have solved several hard perceptual problems in robotics. Early successes of particle filters were limited to low-dimensional estimation problems, such as the problem of robot localization in environments with known maps. More recently, researchers have begun exploiting structural properties of robotic domains that have led to successful particle filter applications in spaces with as many as 100,000 dimensions. The fact that every model--no mater how detailed--fails to capture the full complexity of even the most simple robotic environments has lead to specific tricks and techniques essential for the success of particle filters in robotic domains. This article surveys some of these recent innovations, and provides pointers to in-depth articles on the use of particle filters in robotics.
['Sebastian Thrun']
Particle filters in robotics