4 citations · 5 across the 5 of their papers we have counts for
5 papers
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…
KILO-EKF: Koopman-Inspired Learned Observations Extended Kalman Filter
Zi Cong Guo, James R. Forbes, Timothy D. Barfoot
We present the Koopman-Inspired Learned Observations Extended Kalman Filter (KILO-EKF), which combines a standard EKF prediction step with a correction step based on a Koopman-insp…
Marginalizing and Conditioning Gaussians onto Linear Approximations of Smooth Manifolds with Applications in Robotics
Zi Cong Guo, James R. Forbes, Timothy D. Barfoot
We present closed-form expressions for marginalizing and conditioning Gaussians onto linear manifolds, and demonstrate how to apply these expressions to smooth nonlinear manifolds…
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…
Koopman Linearization for Data-Driven Batch State Estimation of Control-Affine Systems
Zi Cong Guo, Vassili Korotkine, James R. Forbes +1
We present the Koopman State Estimator (KoopSE), a framework for model-free batch state estimation of control-affine systems that makes no linearization assumptions, requires no pr…