Pete Lommel is the Hivemind Edge Technical Architect at Shield AI. He has over two decades of experience in planning, navigation and mapping. Including designing efficient algorithms for state-representation and mapping, to enable the creation of common world models by multi-agent systems with network bandwidth, and computing constraints. He has also designed and fielded high performance, real-time vision-aided navigation algorithms including approaches using visual inertial odometry and geo-referenced image correlation. Prior to joining Shield AI, Pete worked as a Senior Member of the Technical Staff at Honeywell and Draper Laboratory. He holds his B.S. in Aerospace Engineering and Mechanics from University of Minnesota and his M.S. in Aeronautics and Astronautics from MIT.
Shield AI Fundamentals: On the Evolution of State Estimation
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.
Shield AI Fundamentals: On the Challenges of State Estimation
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.
Shield AI Fundamentals: On State Estimation
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 is State Estimation in Robotics?
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.