FPPF uses a learned conditional generative proposal approximating the optimal proposal in particle filters, with tractable likelihoods for Bayesian updates and localization for high dimensions, outperforming baselines on nonlinear non-Gaussian systems.
Wiley-Interscience
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An SE(3)-based invariant EKF is formulated for multi-link manipulator state estimation with autonomous error dynamics, modular per-link structure, state-dependent noise, and exponential mean-square boundedness.
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Generative Model Proposal based Particle Filtering for Data Assimilation
FPPF uses a learned conditional generative proposal approximating the optimal proposal in particle filters, with tractable likelihoods for Bayesian updates and localization for high dimensions, outperforming baselines on nonlinear non-Gaussian systems.
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Invariant Stochastic Filtering on SE(3) for Inertial-Encoder State Estimation of Serial Rigid Manipulators
An SE(3)-based invariant EKF is formulated for multi-link manipulator state estimation with autonomous error dynamics, modular per-link structure, state-dependent noise, and exponential mean-square boundedness.