Differentiable Factor Graph Optimization for Learning Smoothers
Brent Yi,Michelle A. Lee,Alina Kloss,Roberto Martín-Martín,Jeannette Bohg,Brent Yi,Michelle A. Lee,Alina Kloss,Roberto Martín-Martín,Jeannette Bohg
A recent line of work has shown that end-to-end optimization of Bayesian filters can be used to learn state estimators for systems whose underlying models are difficult to hand-design or tune, while retaining the core advantages of probabilistic state estimation. As an alternative approach for state estimation in these settings, we present an end-to-end approach for learning state estimators model...
