What performance guarantees exist for algorithms running on complex robot systems that operate in dynamic environments shared with humans and other autonomous agents? This critical question motivates Matt’s work on safe robotic perception and estimation. Matt completed his Master’s degree in Aeronautical engineering at MIT, where he researched resource-efficient simultaneous localization and mapping (SLAM) with the Aerospace Controls Laboratory. His work focused on optimal communication and computation for multi-robot systems using SLAM in challenging missions like wilderness search and rescue.
Currently, Matt is investigating extensions to recent breakthroughs in certifiably globally optimal SLAM to problems involving extrinsic sensor calibration and landmark-based SLAM. This will lead to robots that are able to verify the quality of their model of the world and take action to correct any shortcomings. Matt is also interested in deriving bounds on measurement noise that ensure observability and fast, globally optimal solutions to key robotic estimation problems. These rigorous formal methods, when combined with state-of-the-art learning-based solutions to problems, will form a high-performance and provably safe architecture for mobile autonomous systems. Matt has worked on several projects including:
Sensor Calibration for Robotic Systems
Jacob Lambert, Lee Clement, Matthew Giamou, Jonathan Kelly
MFI 2016. Baden-Baden.
Matthew Giamou, Ziye Ma, Valentin Peretroukhin, Jonathan Kelly
IEEE RA-L 2019.
Resource-Efficient Communication for Multi-Robot SLAM
Matthew Giamou, Kasra Khosoussi, Jonathan How
ICRA 2018. Brisbane.
Yulun Tian, Kasra Khosoussi, Matthew Giamou, Jonathan How, Jonathan Kelly
RSS 2018. Pittsburgh.