Over your career, what have you witnessed with respect to the evolution of state estimation? State estimation systems have become smaller, more ubiquitous, and have integrated new sensors.  Much of this was driven by GPS. Before GPS, state estimation systems were often built around large, expensive sensors, and found application in large, expensive vehicles, such as aircraft and spacecraft.  GPS provided a small and cheap system which enabled localization almost anywhere on the planet.

What makes state estimation challenging? In state estimation we generally have a number of sources of information and our job is to fuse them together into a best estimate.  This is challenging because each source of information is imperfect, incomplete, and indirect.    

What is state estimation? In robotics, state refers to the condition of the robot. The most common conditions we are concerned with are location, velocity, and orientation, but there are many other conditions that could be included in the description of a robot’s state -- temperature, remaining battery life and component faults, to name a few.

What excites you about the work you do with state estimation at Shield AI? Pushing the envelope.  GPS-denied navigation and mapping have been an active research topic for years.  Researchers have made great strides, and, building on top of that work, we are able to demonstrate really compelling autonomous capabilities. Of course, there is still much to be done.  We are striving to achieve truly anytime, anywhere navigation and mapping capabilities.