Path Integral Particle Filtering for Hybrid Systems via Saltation Matrices
This provides a reliable estimation algorithm for hybrid systems with stochastic dynamics, addressing a domain-specific problem with incremental improvements.
The authors tackled state estimation in hybrid systems with intermittent contact by developing an optimal-control-based particle filtering method that uses saltation matrices to handle uncertainty during contact events, resulting in a computationally efficient algorithm that consistently outperforms strong baselines in experiments.
We present an optimal-control-based particle filtering method for state estimation in hybrid systems that undergo intermittent contact with their environments. We follow the path integral filtering framework that exploits the duality between the smoothing problem and optimal control. We leverage saltation matrices to map out the uncertainty propagation during contact events for hybrid systems. The resulting path integral optimal control problem allows for a state estimation algorithm robust to outlier effects, flexible to non-Gaussian noise distributions, that also handles the challenging contact dynamics in hybrid systems. This work offers a computationally efficient and reliable estimation algorithm for hybrid systems with stochastic dynamics. We also present extensive experimental results demonstrating that our approach consistently outperforms strong baselines across multiple settings.