Algorithms for state estimation of humanoid robots usually assume that the feet remain flat and in a constant position while in contact with the ground. However, this hypothesis is easily violated while walking, especially for human-like gaits with heel-toe motion. This reduces the time during which the contact assumption can be used, or requires higher variances to account for errors. In this paper, we present a novel state estimator based on the extended Kalman filter that can properly handle any contact configuration. We consider multiple inertial measurement units (IMUs) distributed throughout the robot's structure, including on both feet, which are used to track multiple bodies of the robot. This multi-IMU instrumentation setup also has the advantage of allowing the deformations in the robot's structure to be estimated, improving the kinematic model used in the filter. The proposed approach is validated experimentally on the exoskeleton Atalante and is shown to present low drift, performing better than similar single-IMU filters. The obtained trajectory estimates are accurate enough to construct elevation maps that have little distortion with respect to the ground truth.

翻译：人形机器人状态估计算法通常假设足部在接触地面时保持水平且位置不变。然而，这一假设在行走过程中容易被违反，尤其是对于具有脚跟-脚尖运动的人类步态。这会导致可应用接触假设的时间缩短，或需要更高的方差来补偿误差。本文提出一种基于扩展卡尔曼滤波的新型状态估计器，能够妥善处理任意接触构型。我们考虑分布于机器人结构中的多个惯性测量单元（IMU），包括位于双脚上的传感器，用于追踪机器人的多个刚体。这种多IMU传感器配置还具有可估计机器人结构变形的优势，从而改进滤波器中使用的运动学模型。所提方法在外骨骼Atalante上进行了实验验证，结果表明其漂移量小，性能优于同类单IMU滤波器。获得的轨迹估计精度足以构建与地面真值偏差极小的高程地图。