论文标题
可区分粒子过滤器的端到端半监督学习
End-To-End Semi-supervised Learning for Differentiable Particle Filters
论文作者
论文摘要
将神经网络纳入粒子过滤器的最新进展为将粒子过滤器应用于大型现实世界应用中提供了预期的灵活性。该框架中的动态和测量模型可以通过粒子过滤器的可区分实现来学习。过去优化此类模型的努力通常需要了解真实的知识，而实际状态可能在实践中获得甚至无法获得。在本文中，为了减少对注释数据的需求，我们基于伪样函数的最大化提出了一个端到端的学习目标，当真实状态的很大一部分未知时，可以改善状态的估计。我们评估具有模拟和现实世界数据集的机器人技术中的状态估计任务中所提出的方法的性能。
Recent advances in incorporating neural networks into particle filters provide the desired flexibility to apply particle filters in large-scale real-world applications. The dynamic and measurement models in this framework are learnable through the differentiable implementation of particle filters. Past efforts in optimising such models often require the knowledge of true states which can be expensive to obtain or even unavailable in practice. In this paper, in order to reduce the demand for annotated data, we present an end-to-end learning objective based upon the maximisation of a pseudo-likelihood function which can improve the estimation of states when large portion of true states are unknown. We assess performance of the proposed method in state estimation tasks in robotics with simulated and real-world datasets.
