We use the mathematics of probability and estimation to allow computers in robots to interpret data from sensors like cameras, radars and lasers, aerial photos and on-the-fly internet queries. We use machine learning techniques to build and calibrate mathematical models which can explain the robot’s view of the world in terms of prior experience (training), prior knowledge (aerial images, road plans and semantics) and automatically generated web queries. We wish to produce technology which allows robots always to know precisely where they are and what is around them.
Already, robots carry goods around factories and manage our ports, but these are constrained, controlled and highly managed workspaces. Here, the navigation task is made simple by installing reflective beacons or guide wires. Our goal is to extend the reach of robot navigation to truly vast scales without the need for such expensive, awkward and inconvenient modification of the environment. It is about enabling machines to operate for, with and beside us in the multitude of spaces we inhabit, live and work.
Why not use GPS?
Even when GPS is available, it does not offer the accuracy required for robots to make decisions about how and when to move safely. Even if it did, it would say nothing about what is around the robot, and that has a massive impact on autonomous decision-making.
Perhaps the ultimate application is civilian transport systems. We are not condemned to a future of congestion and accidents. We will eventually have cars that can drive themselves, interacting safely with other road users and using roads efficiently, thus freeing up our precious time. But to do this the machines need life-long infrastructure-free navigation, and that is the focus of this work.