论文标题
朝着可区分的重采样
Towards Differentiable Resampling
论文作者
论文摘要
重采样是粒子过滤器中基于样本的递归状态估计的关键组成部分。最近的工作探索了可区分的粒子过滤器，以进行端到端学习。但是，在这些作品中，重新采样仍然是一个挑战，因为它本质上是不可差异的。我们通过用博学的神经网络重采样器代替传统重新采样来应对这一挑战。我们提出了一种新型的网络结构，即粒子变压器，并使用基于似然损耗函数在一组粒子上进行训练进行粒子重采样。合并到可区分的粒子过滤器中，我们的模型可以通过梯度下降与其他粒子滤光片组件共同优化。我们的结果表明，在合成数据和模拟机器人本地化任务中，我们学到的重采样器优于传统的重采样技术。
Resampling is a key component of sample-based recursive state estimation in particle filters. Recent work explores differentiable particle filters for end-to-end learning. However, resampling remains a challenge in these works, as it is inherently non-differentiable. We address this challenge by replacing traditional resampling with a learned neural network resampler. We present a novel network architecture, the particle transformer, and train it for particle resampling using a likelihood-based loss function over sets of particles. Incorporated into a differentiable particle filter, our model can be end-to-end optimized jointly with the other particle filter components via gradient descent. Our results show that our learned resampler outperforms traditional resampling techniques on synthetic data and in a simulated robot localization task.
