10 citations · 29 across the 20 of their papers we have counts for
20 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…
Sliding Sensors: Configurable Confidence in State Estimation for Continuum Robots
Ella Walsh, Spencer Teetaert, Eric Diller +2
Continuum robots often operate in uncertain environments, where accurate state estimation is essential for safe interactions. Estimate uncertainty is inherently spatially non-unifo…
A Sliding-Window Filter for Online Continuous-Time Continuum Robot State Estimation
Spencer Teetaert, Sven Lilge, Jessica Burgner-Kahrs +1
Stochastic state estimation methods for continuum robots (CRs) often struggle to balance accuracy and computational efficiency. While several recent works have explored sliding-win…
Sharing the Load: Autonomous Multi-Rover Cargo Transport
Alexander Krawciw, Luka Antonyshyn, Sven Lilge +5
A future lunar habitat, as part of the Artemis program, will require a significant amount of logistics infrastructure. Cargo that is transported to the Moon will need to be moved f…
aUToPath: Unified Planning and Control for Autonomous Vehicles in Urban Environments Using Hybrid Lattice and Free-Space Search
Tanmay P. Patel, Connor Wilson, Ellina R. Zhang +4
This paper presents aUToPath, a unified online framework for global path-planning and control to address the challenge of autonomous navigation in cluttered urban environments. A k…
UAV See, UGV Do: Aerial Imagery and Virtual Teach Enabling Zero-Shot Ground Vehicle Repeat
Desiree Fisker, Alexander Krawciw, Sven Lilge +2
This paper presents Virtual Teach and Repeat (VirT&R): an extension of the Teach and Repeat (T&R) framework that enables GPS-denied, zero-shot autonomous ground vehicle navigation…
Continuous-Time State Estimation Methods in Robotics: A Survey
William Talbot, Julian Nubert, Turcan Tuna +5
Accurate, efficient, and robust state estimation is more important than ever in robotics as the variety of platforms and complexity of tasks continue to grow. Historically, discret…
A Third-Order Gaussian Process Trajectory Representation Framework with Closed-Form Kinematics for Continuous-Time Motion Estimation
Thien-Minh Nguyen, Ziyu Cao, Kailai Li +5
In this paper, we propose a third-order, i.e., white-noise-on-jerk, Gaussian Process (GP) Trajectory Representation (TR) framework for continuous-time (CT) motion estimation (ME) t…
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,…
Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?
Daniil Lisus, Keenan Burnett, David J. Yoon +3
Spinning, frequency-modulated continuous-wave (FMCW) radars with 360 degree coverage have been gaining popularity for autonomous-vehicle navigation. However, unlike `fixed' automot…
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm
Ziyu Zhang, Johann Laconte, Daniil Lisus +1
This paper presents a novel method for assessing the resilience of the ICP algorithm via learning-based, worst-case attacks on lidar point clouds. For safety-critical applications…
Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior
Keenan Burnett, Angela P. Schoellig, Timothy D. Barfoot
In this work, we demonstrate continuous-time radar-inertial and lidar-inertial odometry using a Gaussian process motion prior. Using a sparse prior, we demonstrate improved computa…
State Estimation for Continuum Multi-Robot Systems on SE(3)
Sven Lilge, Timothy D. Barfoot, Jessica Burgner-Kahrs
In contrast to conventional robots, accurately modeling the kinematics and statics of continuum robots is challenging due to partially unknown material properties, parasitic effect…
Off the Beaten Track: Laterally Weighted Motion Planning for Local Obstacle Avoidance
Jordy Sehn, Timothy D. Barfoot, Jack Collier
We extend the behaviour of generic sample-based motion planners to support obstacle avoidance during long-range path following by introducing a new edge-cost metric paired with a c…
Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights
Daniil Lisus, Johann Laconte, Keenan Burnett +2
This paper presents a novel deep-learning-based approach to improve localizing radar measurements against lidar maps. This radar-lidar localization leverages the benefits of both s…
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…
DeepMEL: Compiling Visual Multi-Experience Localization into a Deep Neural Network
Mona Gridseth, Timothy D. Barfoot
Vision-based path following allows robots to autonomously repeat manually taught paths. Stereo Visual Teach and Repeat (VT\&R) accomplishes accurate and robust long-range path foll…