7 papers
Exploiting Chordal Sparsity for Globally Optimal Estimation with Factor Graphs
Avinash Subramanian, Connor Holmes, Timothy D. Barfoot +2
Robust and efficient state estimation is crucial for perception, navigation, and control in robotics. State estimation problems are conveniently modeled using the factor-graph fram…
KSOS-BO: Improving Sampling in Bayesian Optimization via Kernel Sum of Squares
Buqing Ou, Frederike Dümbgen
Bayesian Optimization (BO) is an effective framework for globally optimizing functions whose evaluations are expensive. It is particularly effective for optimizing functions define…
Sampling-Based Global Optimal Control and Estimation via Semidefinite Programming
Antoine Groudiev, Fabian Schramm, Éloïse Berthier +2
Global optimization has gained attraction over the past decades, thanks to the development of both theoretical foundations and efficient numerical routines. Among recent advances,…
Exploiting Chordal Sparsity for Fast Global Optimality with Application to Localization
Frederike Dümbgen, Connor Holmes, Timothy D. Barfoot
In recent years, many estimation problems in robotics have been shown to be solvable to global optimality using their semidefinite relaxations. However, the runtime complexity of o…
Safe and Efficient Estimation for Robotics through the Optimal Use of Resources
Frederike Dümbgen
In order to operate in and interact with the physical world, robots need to have estimates of the current and future state of the environment. We thus equip robots with sensors and…
SDPRLayers: Certifiable Backpropagation Through Polynomial Optimization Problems in Robotics
Connor Holmes, Frederike Dümbgen, Timothy D. Barfoot
A recent set of techniques in the robotics community, known as certifiably correct methods, frames robotics problems as polynomial optimization problems (POPs) and applies convex,…