Zi Cong Guo — accepted AI-conference papers

2025

Marginalizing and Conditioning Gaussians onto Linear Approximations of Smooth Manifolds with Applications in Robotics

Zi Cong Guo, James Richard Forbes, Timothy D. Barfoot

ICRA 2025

We present closed-form expressions for marginalizing and conditioning Gaussians onto linear manifolds, and demonstrate how to apply these expressions to smooth non-linear manifolds through linearization. Although marginalization and conditioning onto axis-aligned manifolds are well-established proce

Cited by 3 Source ↗code ↗Scholar ↗

2022

Koopman Linearization for Data-Driven Batch State Estimation of Control-Affine Systems

Zi Cong Guo, Vassili Korotkine, James Richard Forbes, Timothy D. Barfoot

RA-L 2022

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 problem-specific feature selections, and has an inference computational cost that is independent of the number of training

Cited by 16 Source ↗Scholar ↗