18 citations · 61 across the 15 of their papers we have counts for
15 papers
RAYA: Learning Where and When to Intervene for Robot Recovery
Ishaan Mahajan, Charles Chen, Frederike Dümbgen +1
A robot can predict failure and still be unable to prevent it. By the time a safety mechanism reacts, the nominal plan may already have spent the control authority that recovery re…
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,…
Data-Driven Batch Localization and SLAM Using Koopman Linearization
Zi Cong Guo, Frederike Dümbgen, James R. Forbes +1
We present a framework for model-free batch localization and SLAM. We use lifting functions to map a control-affine system into a high-dimensional space, where both the process mod…
Certifiably Optimal Rotation and Pose Estimation Based on the Cayley Map
Timothy D Barfoot, Connor Holmes, Frederike Dümbgen
We present novel, convex relaxations for rotation and pose estimation problems that can a posteriori guarantee global optimality for practical measurement noise levels. Some such r…
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…
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…
What to Learn: Features, Image Transformations, or Both?
Yuxuan Chen, Binbin Xu, Frederike Dümbgen +1
Long-term visual localization is an essential problem in robotics and computer vision, but remains challenging due to the environmental appearance changes caused by lighting and se…
Blind as a bat: audible echolocation on small robots
Frederike Dümbgen, Adrien Hoffet, Mihailo Kolundžija +2
For safe and efficient operation, mobile robots need to perceive their environment, and in particular, perform tasks such as obstacle detection, localization, and mapping. Although…
Safe and Smooth: Certified Continuous-Time Range-Only Localization
Frederike Dümbgen, Connor Holmes, Timothy D. Barfoot
A common approach to localize a mobile robot is by measuring distances to points of known positions, called anchors. Locating a device from distance measurements is typically posed…
A Fine Line: Total Least-Squares Line Fitting as QCQP Optimization
Timothy D Barfoot, Connor Holmes, Frederike Dumbgen
This note uses the Total Least-Squares (TLS) line-fitting problem as a canvas to explore some modern optimization tools. The contribution is meant to be tutorial in nature. The TLS…