仅用自身体感传感器，实时估算人形机器人在晃动地面上的位置与速度。
Proprioceptive Invariant State Estimation for Humanoid Robots on Non-Inertial Ground
- 基于足部惯性测量，构建不变扩展卡尔曼滤波器估计相对位置。
- 在晃动地面实验中，收敛速度提升96%，位置误差降低80%。
- 适合动态环境中的人形机器人状态估计，无需外部传感器。
本文提出一种基于不变扩展卡尔曼滤波（InEKF）的方法，仅使用机载本体感受传感信息，实现实时状态估计，适用于在非惯性地面运动的人形机器人。该方法通过足部安装的IMU利用步态接触点的运动学约束，估计机器人基座相对于移动地面坐标系的位置和速度，无需直接获取地面运动数据或外置传感器。滤波器设计为右不变测量模型，可在初始不确定性较大的情况下实现稳定的误差动力学。可观测性分析明确了在非惯性地面框架下，机器人相对基座位置与速度可被观测的条件。在Digit人形机器人站在晃动与俯仰地面进行站立与深蹲实验中，收敛速率提升96%，位置估计误差减少80%；在单轴旋转地面上步行实验中，初始误差达1米时，平均估计误差仍低于9厘米。
原文摘要 · Abstract (English)
This paper presents an invariant extended Kalman filtering (InEKF) approach for real-time state estimation of humanoid robots operating on non-inertial ground using only onboard proprioceptive sensing. The proposed approach estimates the robot's base position and velocity relative to the moving ground frame without requiring direct measurements of ground motion or externally mounted sensors. By exploiting kinematic constraints at the stance foot through foot-mounted IMUs, the filter accounts for ground-induced nonlinearities in the process and measurement models while remaining fully proprioceptive. The estimator is formulated to admit a right-invariant measurement model, enabling favorable error dynamics under large initial uncertainties. Observability analysis establishes conditions under which the robot's relative base position and velocity are observable with respect to the non-inertial ground frame. Experiments with the Digit humanoid robot standing and squatting atop a swaying and pitching ground showcase a 96% speedup in convergence rate and an 80% reduction in position estimate errors over existing InEKFs. Walking experiments on a uni-axially rotating ground achieve an average estimation error of less than 9 cm for an initial error of up to 1 m.
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