If mobile robots are to become ubiquitous, we must first solve fundamental problems in perception. Before a mobile robot system can act intelligently, it must be given — or acquire — a representation of the environment that is useful for planning and control. Perception comes before action, and the perception problem is one of the most difficult we face.
We study probabilistic perception algorithms and estimation theory that enable long-term autonomous operation of mobile robotic systems, particularly in unknown environments. We have extensive experience with vision based, real-time localization and mapping systems, and are interested in fundamental understanding of sufficient statistics that can be used to represent the state of the world.
An important goal in mobile robotics is the development of perception algorithms that allow for persistent, long-term autonomous operation in unknown situations (over weeks or more). In our effort to achieve long-term autonomy, we have had to solve problems of both metric and semantic estimation. We use real-time, embodied robot systems equipped with a variety of sensors — including lasers, cameras, inertial sensors, etc. — to advance and validate algorithms and knowledge representations that are useful for enabling long-term autonomous operation.