Residual-Based Adaptive Kalman Filtering for Legged Robot State Estimation
2026-08-03 • Robotics
Robotics
AI summaryⓘ
The authors address the challenge of estimating the position and movement of walking robots, which is important for control but tricky because the robot’s sensors can be noisy. They improve a common method called the Kalman filter by letting it adjust how much it trusts sensors and its own model automatically, instead of using fixed settings. Testing on a four-legged robot showed that adapting the measurement noise gives better accuracy without needing extra sensors or complicated tuning. Their method works well for different walking styles and environments.
State estimationKalman filterProcess noise covarianceMeasurement noise covarianceInvariant Extended Kalman Filter (InEKF)IMULeg kinematicsRobot gaitResidual-based adaptationQuadruped robot
Authors
Mihaela Popescu, Dennis Mronga, Shivesh Kumar, Frank Kirchner
Abstract
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.