This paper introduces a novel state estimation framework for robots using differentiable ensemble Kalman filters (DEnKF). DEnKF is a reformulation of the traditional ensemble Kalman filter that employs stochastic neural networks to model the process noise implicitly. Our work is an extension of previous research on differentiable filters, which has provided a strong foundation for our modular and end-to-end differentiable framework. This framework enables each component of the system to function independently, leading to improved flexibility and versatility in implementation. Through a series of experiments, we demonstrate the flexibility of this model across a diverse set of real-world tracking tasks, including visual odometry and robot manipulation. Moreover, we show that our model effectively handles noisy observations, is robust in the absence of observations, and outperforms state-of-the-art differentiable filters in terms of error metrics. Specifically, we observe a significant improvement of at least 59% in translational error when using DEnKF with noisy observations. Our results underscore the potential of DEnKF in advancing state estimation for robotics. Code for DEnKF is available at https://github.com/ir-lab/DEnKF

翻译：本文提出了一种新颖的机器人状态估计框架，该框架采用可微分集成卡尔曼滤波器（DEnKF）。DEnKF是对传统集成卡尔曼滤波器的重新表述，利用随机神经网络隐式建模过程噪声。我们的工作是对可微分滤波器前期研究的扩展，这些研究为我们的模块化且端到端可微分的框架奠定了坚实基础。该框架使系统的每个组件能够独立运行，从而提高了实现的灵活性和通用性。通过一系列实验，我们展示了该模型在多种真实世界追踪任务中的灵活性，包括视觉里程计和机器人操作。此外，我们证明该模型能有效处理带噪声的观测值，在观测缺失时具有鲁棒性，并且在误差指标上优于当前最先进的可微分滤波器。具体而言，在带噪声观测条件下使用DEnKF时，平移误差至少显著降低了59%。我们的结果凸显了DEnKF在推进机器人状态估计领域的潜力。DEnKF的代码可在https://github.com/ir-lab/DEnKF获取。