Evaluating the Roomba: A low-cost, ubiquitous platform for robotics research and education

This paper presents the iRobot corporation's Roomba vacuum as a low-cost resource for robotics research and education. Sensor and actuation models for unmodified Roombas are presented in the context of both special- and general-purpose spatial-reasoning algorithms, including Monte Carlo Localization and FastSLAM. Further tests probe the feasibility of sensor extensions to the platform. Results demonstrate that, with some caveats, the Roomba is a viable foundation for both classroom and laboratory use, especially for work seeking to leverage robots to other ends, as well as robotics per se with a computational focus.