20 citations · 60 across the 13 of their papers we have counts for
13 papers
Following a Unique Path: A Fast Certifier Applied to Outlier-Robust Pose Registration
Connor Holmes, Abhishek Goudar, Timothy D. Barfoot
Certifiable methods have arisen as a means to guarantee global optimality of solutions to non-convex problems using convex semidefinite programming (SDP) relaxations. The most perf…
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…
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,…
STAR-loc: Dataset for STereo And Range-based localization
Frederike Dümbgen, Mohammed A. Shalaby, Connor Holmes +4
This document contains a detailed description of the STAR-loc dataset. For a quick starting guide please refer to the associated Github repository (https://github.com/utiasASRL/sta…
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…
Towards Open World NeRF-Based SLAM
Daniil Lisus, Connor Holmes, Steven Waslander
Neural Radiance Fields (NeRFs) offer versatility and robustness in map representations for Simultaneous Localization and Mapping (SLAM) tasks. This paper extends NICE-SLAM, a recen…
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…
An Efficient Global Optimality Certificate for Landmark-Based SLAM
Connor Holmes, Timothy D. Barfoot
Modern state estimation is often formulated as an optimization problem and solved using efficient local search methods. These methods at best guarantee convergence to local minima,…