2 citations · 4 across the 3 of their papers we have counts for
7 papers · 1 filter
Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning
Keenan Burnett, David J. Yoon, Angela P. Schoellig +1
This paper presents a radar odometry method that combines probabilistic trajectory estimation and deep learned features without needing groundtruth pose information. The feature ne…
Unsupervised Learning of Lidar Features for Use in a Probabilistic Trajectory Estimator
David J. Yoon, Haowei Zhang, Mona Gridseth +2
We present unsupervised parameter learning in a Gaussian variational inference setting that combines classic trajectory estimation for mobile robots with deep learning for rich sen…
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…
Exactly Sparse Gaussian Variational Inference with Application to Derivative-Free Batch Nonlinear State Estimation
Timothy D. Barfoot, James R. Forbes, David Yoon
We present a Gaussian Variational Inference (GVI) technique that can be applied to large-scale nonlinear batch state estimation problems. The main contribution is to show how to fi…
Mapless Online Detection of Dynamic Objects in 3D Lidar
David J. Yoon, Tim Y. Tang, Timothy D. Barfoot
This paper presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data. We explicitly compensate for the moving-while-scanning operat…