用双层优化同时校准机器人状态估计的噪声与运动参数，提升精度。
Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization
- 双层优化框架联合调整噪声协方差和运动模型参数
- 相比手动调参，状态估计误差显著降低，不确定性更准确
- 适用于四足与人形机器人，适合需要高精度感知的场景
在动态不确定环境中，精确的状态估计对腿式和飞行机器人至关重要。核心挑战在于难以确定过程与测量噪声协方差，通常需手动设定。本文提出一种双层优化框架，以“估计器嵌套”方式联合校准协方差矩阵与运动学参数。上层将噪声协方差与模型参数视为优化变量，下层执行全信息估计算法。通过反向传播经过估计算法，可直接优化轨迹级目标，获得精确且一致的状态估计。我们在四足与人形机器人上验证了该方法，相比手工调参基线，显著提升了估计精度与不确定性校准能力。该方法将状态估计、传感器与运动学校准统一为一个数据驱动的普适框架，适用于多种机器人平台。
原文摘要 · Abstract (English)
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.
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