Refresh: The Factor-Fuse-Forget Filtering Framework

Estimating and tracking the state is a fundamental ability of any autonomous system. The complexity of state spaces in typical applications frequently necessitate approximate filtering techniques such as particle filters. However, these methods often suffer from the curse of dimensionality when attempting to maintain a high-dimensional joint posterior distribution. Based on the insight that most joint distributions are projected into low-dimensional marginals when used, we propose to actively eschew the joint space, and perform the bulk of filtering in a factored state space, where each factor gives rise to a low-dimensional marginal distribution that is hopefully easier to track. Our framework supports fusing these factors to temporarily obtain joint posterior distributions, but forgets these outputs in favor of maintaining a tractable factored filter. In simulated domains, we demonstrate that this framework offers substantial improvements compared to existing factoring and particle filtering methods, in both filtering accuracy and computational efficiency.