自适应卡尔曼滤波提升机器狗状态估计精度，无需额外传感器。
Residual-Based Adaptive Kalman Filtering for Legged Robot State Estimation
- 基于残差与创新的在线噪声自适应，动态调整过程与测量噪声。
- 在四足机器人跑步步态下，误差降低25%，效果媲美力传感器方案。
- 仅需惯性与关节信息，无需足部力传感器或人工调参，适合实际部署。
状态估计是基于模型的步行机器人控制中的关键环节，广泛适用于隐变量推断场景。卡尔曼滤波常用于融合多源感知信息，估计浮动基座的位置与速度。然而，噪声参数调优困难且通常依赖专家经验；固定参数难以适应不同步态与环境变化。本文提出一种在线自适应策略，用于过程噪声协方差矩阵Q与测量噪声协方差矩阵R的动态调整。具体地，引入基于滤波残差与创新的协方差自适应方法，并在融合IMU与腿运动学信息的不变扩展卡尔曼滤波器（InEKF）中实现。在室内与室外数据集上对Unitree Go2四足机器人进行实验验证，结果表明仅自适应测量噪声协方差R即可使跑步步态下的估计精度提升25%，显著优于固定调参的InEKF。最终，该方法性能接近使用足力传感器的方法，且无需足力测量或额外参数调优。
原文摘要 · Abstract (English)
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.
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