In this letter, we propose an Attention-Based Neural-Augmented Kalman Filter (AttenNKF) for state estimation in legged robots. Foot slip is a major source of estimation error: when slip occurs, kinematic measurements violate the no-slip assumption and inject bias during the update step. Our objective is to estimate this slip-induced error and compensate for it. To this end, we augment an Invariant Extended Kalman Filter (InEKF) with a neural compensator that uses an attention mechanism to infer error conditioned on foot-slip severity and then applies this estimate as a post-update compensation to the InEKF state (i.e., after the filter update). The compensator is trained in a latent space, which aims to reduce sensitivity to raw input scales and encourages structured slip-conditioned compensations, while preserving the InEKF recursion. Experiments demonstrate improved performance compared to existing legged-robot state estimators, particularly under slip-prone conditions.

翻译：本文提出了一种基于注意力的神经增强卡尔曼滤波器（AttenNKF），用于腿式机器人的状态估计。足部滑移是估计误差的主要来源：当滑移发生时，运动学测量违背了无滑移假设，并在更新步骤中引入了偏差。我们的目标是估计这种滑移引起的误差并对其进行补偿。为此，我们在不变扩展卡尔曼滤波器（InEKF）基础上，增加了一个神经补偿器，该补偿器利用注意力机制根据足部滑移严重程度推断误差，然后将此估计值作为后更新补偿应用于InEKF状态（即在滤波器更新之后）。补偿器在潜在空间中进行训练，旨在降低对原始输入尺度的敏感性，并鼓励结构化的滑移条件补偿，同时保持InEKF的递归特性。实验结果表明，与现有的腿式机器人状态估计器相比，该方法性能有所提升，尤其在易发生滑移的条件下表现更为突出。