We present differentiable particle filters (DPFs): a differentiable
implementation of the particle filter algorithm with learnable motion and
measurement models. Since DPFs are end-to-end differentiable, we can
efficiently train their models by optimizing end-to-end state estimation
performance, rather than proxy objectives such as model accuracy. DPFs encode
the structure of recursive state estimation with prediction and measurement
update that operate on a probability distribution over states. This structure
represents an algorithmic prior that improves learning performance in state
estimation problems while enabling explainability of the learned model. Our
experiments on simulated and real data show substantial benefits from end-to-
end learning with algorithmic priors, e.g. reducing error rates by ~80%. Our
experiments also show that, unlike long short-term memory networks, DPFs learn
localization in a policy-agnostic way and thus greatly improve generalization.
Source code is available at
this https URL .

本研究介绍了可微分粒子滤波器（DPFs），用于在概率分布之上进行的递归状态估计的算法。我们证明了不同的学习模型如动态和测量模型，能在 end-to-end 的不同 iable 框架下完成。当前的实验表明，与 LSTM 相比，DPFs 能够更好地维持位置估计并能提高算法的泛化性。