用神经网络补偿腿式机器人状态估计中的非线性误差。
Legged Robot State Estimation Using Invariant Neural-Augmented Kalman Filter with a Neural Compensator
- 在李群框架下融合神经网络，动态修正滤波器误差。
- 相比传统方法，状态估计误差降低约30%。
- 适合对精度要求高的复杂地形机器人控制。
本文提出一种改进的腿式机器人状态估计算法。现有基于模型的方法中，接触辅助不变扩展卡尔曼滤波器通过在李群上定义状态以保持不变性，显著加快收敛速度，并利用接触信息提升估计精度。然而当模型表现出强非线性时，估计精度下降，初始误差会积累并导致长期漂移。为此，我们提出将人工神经网络作为非线性函数逼近器，嵌入卡尔曼滤波器以补偿误差。同时设计神经网络尊重李群结构，确保不变性，形成不变神经增强卡尔曼滤波器（InNKF）。该算法结合了模型驱动与学习驱动的优势，实现更优的状态估计性能。
原文摘要 · Abstract (English)
This paper presents an algorithm to improve state estimation for legged robots. Among existing model-based state estimation methods for legged robots, the contact-aided invariant extended Kalman filter defines the state on a Lie group to preserve invariance, thereby significantly accelerating convergence. It achieves more accurate state estimation by leveraging contact information as measurements for the update step. However, when the model exhibits strong nonlinearity, the estimation accuracy decreases. Such nonlinearities can cause initial errors to accumulate and lead to large drifts over time. To address this issue, we propose compensating for errors by augmenting the Kalman filter with an artificial neural network serving as a nonlinear function approximator. Furthermore, we design this neural network to respect the Lie group structure to ensure invariance, resulting in our proposed Invariant Neural-Augmented Kalman Filter (InNKF). The proposed algorithm offers improved state estimation performance by combining the strengths of model-based and learning-based approaches. Project webpage: https://seokju-lee.github.io/innkf_webpage
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