Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization

Accurate state estimation is critical for legged and aerial robots operating in dynamic, uncertain environments.

A key challenge lies in specifying process and measurement noise covariances, which are typically unknown or manually tuned.

In this work, we introduce a bi-level optimization framework that jointly calibrates covariance matrices and kinematic parameters in an estimator-in-the-loop manner.

The upper level treats noise covariances and model parameters as optimization variables, while the lower level executes a full-information estimator.

Differentiating through the estimator allows direct optimization of trajectory-level objectives, resulting in accurate and consistent state estimates.

We validate our approach on quadrupedal and humanoid robots, demonstrating significantly improved estimation accuracy and uncertainty calibration compared to hand-tuned baselines.

Our method unifies state estimation, sensor, and kinematics calibration into a principled, data-driven framework applicable across diverse robotic platforms.