6 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…
Smoothing Out the Edges: Continuous-Time Estimation with Gaussian Process Motion Priors on Factor Graphs
Connor Holmes, Sven Lilge, Zi Cong Guo +2
Continuous-time state estimation is gaining in popularity due to its abilities to provide smooth solutions, handle asynchronous sensors, and interpolate between data points. While…
On Semidefinite Relaxations for Matrix-Weighted State-Estimation Problems in Robotics
Connor Holmes, Frederike Dümbgen, Timothy D Barfoot
In recent years, there has been remarkable progress in the development of so-called certifiable perception methods, which leverage semidefinite, convex relaxations to find global o…
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…
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,…
Toward Globally Optimal State Estimation Using Automatically Tightened Semidefinite Relaxations
Frederike Dümbgen, Connor Holmes, Ben Agro +1
In recent years, semidefinite relaxations of common optimization problems in robotics have attracted growing attention due to their ability to provide globally optimal solutions. I…