Enhancing State Estimation in Robots: A Data-Driven Approach with Differentiable Ensemble Kalman Filters

Understand

This paper introduces a novel state estimation framework for robots using differentiable ensemble Kalman filters (DEnKF).

• DEnKF is a reformulation of the traditional ensemble Kalman filter that employs stochastic neural networks to model the process noise implicitly.
• Our work is an extension of previous research on differentiable filters, which has provided a strong foundation for our modular and end-to-end differentiable framework.
• This framework enables each component of the system to function independently, leading to improved flexibility and versatility in implementation.