发表机构
University of Bremen; German Research Center for Artificial Intelligence (DFKI GmbH); Chalmers University of Technology（不来梅大学; 德国人工智能研究中心; 查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对四足机器人状态估计中卡尔曼滤波噪声参数难调、固定参数不适用于变步态环境的问题，提出基于残差和新息的协方差自适应方法，在InEKF中实现，实验表明其性能与足端力方法相当，trot步态精度提升25%
AI 中文摘要
状态估计是步行机器人基于模型控制的关键组成部分，更广泛地说，适用于任何需要推断隐藏变量的场景。卡尔曼滤波被广泛用于融合多种传感模态，以估计浮动基座的位置和速度。然而，噪声参数的调参极具挑战性，通常需要专业知识，且固定的噪声参数不适用于变化的步态和环境。我们针对过程噪声协方差矩阵Q和测量噪声协方差矩阵R提出了一种在线自适应策略。具体而言，我们引入了一种基于滤波器残差和新息的协方差自适应方法，用于四足机器人状态估计，并将其与依赖IMU和足端力测量的基准方法进行了对比评估。该自适应方法在不变扩展卡尔曼滤波（InEKF）中实现，融合了IMU和腿部运动学。在Unitree Go2四足机器人的室内和室外数据集上进行的实验表明，与固定调参的InEKF相比，针对 trot 步态调整R即可使精度提升25%。最后，所提出的基于残差的自适应方法达到了与足端力方法相当的性能，且无需足端力测量或额外的参数调参。
英文摘要
State estimation is a key component in model-based control of walking robots and, more broadly, applicable wherever hidden variables must be inferred. The Kalman filter is widely used to estimate floating-base position and velocity by fusing multiple sensing modalities. However, tuning noise parameters is challenging and typically requires expert knowledge. Moreover, fixed noise parameters are unsuitable for varying gaits and environments. We propose an online adaptation strategy for the process noise covariance matrix Q and the measurement noise covariance matrix R. Specifically, we introduce a filter residual and innovation-based covariance adaptation method for legged robot state estimation and evaluate it against a baseline approach relying on IMU and foot force measurements. The proposed adaptation is implemented within an Invariant Extended Kalman Filter (InEKF) fusing IMU and leg kinematics. Experiments on indoor and outdoor datasets with a Unitree Go2 quadruped show that adapting R is sufficient and improves accuracy by 25% for the trotting gait compared to the fixed-tuned InEKF. Finally, the proposed residual-based adaptation achieves comparable performance to the foot force approach, without requiring foot force measurements or additional parameter tuning.
