1.2k citations · 2.1k across the 52 of their papers we have counts for
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Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation?
Keenan Burnett, Angela P. Schoellig, Timothy D. Barfoot
In order to tackle the challenge of unfavorable weather conditions such as rain and snow, radar is being revisited as a parallel sensing modality to vision and lidar. Recent works…
Fundamental Linear Algebra Problem of Gaussian Inference
Timothy D Barfoot
Underlying many Bayesian inference techniques that seek to approximate the posterior as a Gaussian distribution is a fundamental linear algebra problem that must be solved for both…
Making Sense of the Robotized Pandemic Response: A Comparison of Global and Canadian Robot Deployments and Success Factors
T. Barfoot, J. Burgner-Kahrs, E. Diller +12
From disinfection and remote triage, to logistics and delivery, countries around the world are making use of robots to address the unique challenges presented by the COVID-19 pande…
Variational Inference as Iterative Projection in a Bayesian Hilbert Space with Application to Robotic State Estimation
Timothy D. Barfoot, Gabriele M. T. D'Eleuterio
Variational Bayesian inference is an important machine-learning tool that finds application from statistics to robotics. The goal is to find an approximate probability density func…
Zeus: A System Description of the Two-Time Winner of the Collegiate SAE AutoDrive Competition
Keenan Burnett, Jingxing Qian, Xintong Du +14
The SAE AutoDrive Challenge is a three-year collegiate competition to develop a self-driving car by 2020. The second year of the competition was held in June 2019 at MCity, a mock…
Variational Inference with Parameter Learning Applied to Vehicle Trajectory Estimation
Jeremy N. Wong, David J. Yoon, Angela P. Schoellig +1
We present parameter learning in a Gaussian variational inference setting using only noisy measurements (i.e., no groundtruth). This is demonstrated in the context of vehicle traje…