Probabilistic localization with a blind robot

Researchers have addressed the localization problem for mobile robots using many different kinds of sensors, including rangefinders, cameras, and odometers. In this paper, we consider localization using a robot that is virtually "blind", having only a clock and contact sensor at its disposal. This represents a drastic reduction in sensing requirements, even in light of existing work that considers localization with limited sensing. We present probabilistic techniques that represent and update the robot\´s position uncertainty and algorithms to reduce this uncertainty. We demonstrate the experimental effectiveness of these methods using a Roomba autonomous vacuum cleaner robot in laboratory environments.