By approximating posterior distributions with weighted samples, particle filters (PFs) provide an efficient mechanism for solving non-linear sequential state estimation problems. While the effectiveness of particle filters has been recognised in various applications, the performance of particle filters relies on the knowledge of dynamic models and measurement models, and the construction of effective proposal distributions. An emerging trend in designing particle filters is the differentiable particle filters (DPFs). By constructing particle filters' components through neural networks and optimising them by gradient descent, differentiable particle filters are a promising computational tool to perform inference for sequence data in complex high-dimensional tasks such as vision-based robot localisation. In this paper, we provide a review of recent advances in differentiable particle filters and their applications. We place special emphasis on different design choices of key components of differentiable particle filters, including dynamic models, measurement models, proposal distributions, optimisation objectives, and differentiable resampling techniques.

翻译：通过使用加权样本近似后验分布，粒子滤波器为求解非线性序贯状态估计问题提供了一种高效机制。尽管粒子滤波器的有效性已在多种应用中得到认可，但其性能依赖于动态模型与测量模型的先验知识，以及有效提议分布的构建。粒子滤波器设计的新兴趋势是可微分粒子滤波器（DPF）。通过利用神经网络构建粒子滤波器组件并采用梯度下降法进行优化，可微分粒子滤波器成为在复杂高维任务（如基于视觉的机器人定位）中对序列数据执行推断的有前景计算工具。本文综述了可微分粒子滤波器的最新进展及其应用，重点阐述了其关键组件的不同设计选择，包括动态模型、测量模型、提议分布、优化目标以及可微分重采样技术。