Matthew Giamou

Ph.D. Student
Department:

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 estimation and planning. 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 applying global polynomial optimization techniques to various estimation and planning problems involving 3D position and orientation. 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 optimization 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 is a Vector Institute Post-Graduate Affiliate, and the recipient of a 2019 Royal Bank of Canada Fellowship. Matt has worked on several projects including:

 

Global Polynomial Optimization for Robot Kinematics

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Convex relaxations for polynomial formulations of inverse kinematics.

Inverse Kinematics for Serial Kinematic Chains via Sum of Squares Optimization
Filip Marić, Matthew Giamou, Soroush Khoubyarian, Ivan Petrović, Jonathan Kelly
arXiv pre-print.

Certifiably Globally Optimal Estimation via Convex Relaxations

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Dual SDP relaxation for extrinsic calibration.

Sparse Bounded Degree Sum of Squares Optimization for Certifiably Globally Optimal Rotation Averaging
Matthew Giamou, Filip Maric, Valentin Peretroukhin, Jonathan Kelly
arXiv pre-print.
Certifiably Globally Optimal Extrinsic Calibration from Per-Sensor Egomotion
Matthew Giamou, Ziye Ma, Valentin Peretroukhin, Jonathan Kelly
IEEE RA-L 2019.

Sensor Calibration for Robotic Systems

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Self-calibration between sensors.

Entropy-Based  Calibration of 2D Lidars to Egomotion Sensors
Jacob Lambert, Lee Clement, Matthew Giamou, Jonathan Kelly
MFI 2016. Baden-Baden.
Certifiably Globally Optimal Extrinsic Calibration from Per-Sensor Egomotion
Matthew Giamou, Ziye Ma, Valentin Peretroukhin, Jonathan Kelly
IEEE RA-L 2019.

Resource-Efficient Communication for Multi-Robot SLAM

RSS 2018.


Measurement exchange graph for multi-robot SLAM.

Talk Resource-Efficiently to Me: Optimal Communication Planning for Distributed SLAM Front-Ends
Matthew Giamou, Kasra Khosoussi, Jonathan How
ICRA 2018. Brisbane.
Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection
Yulun Tian, Kasra Khosoussi, Matthew Giamou, Jonathan How, Jonathan Kelly
RSS 2018. Pittsburgh.