基于 EKF 和 MHE 的足式机器人运动快速去中心化状态估计
Fast Decentralized State Estimation for Legged Robot Locomotion via EKF and MHE
AI总结：
本文提出一种快速去中心化足式机器人状态估计框架，通过 EKF 估计姿态、MHE 估计线速度，并利用 FIF 边缘化方法实现解耦，在保证精度与约束下提升了计算效率。
摘要：
本文提出了一种用于足式运动控制的快速去中心化状态估计框架。浮动基座状态的非线性估计被去中心化为通过 Extended Kalman Filter (EKF) 进行的姿态估计，以及通过 Moving Horizon Estimation (MHE) 进行的线速度估计。EKF 将惯性传感器与视觉融合以估计浮动基座姿态。MHE 利用估计的姿态和过去时间窗口内的所有传感器，基于感兴趣状态且带有状态约束的时变线性动力学公式来估计线速度。更重要的是，提出了一种基于 full information filter (FIF) 优化结构的边缘化方法，将等式约束的 FIF 转换为等效的 MHE。这种状态估计的解耦促进了计算效率、估计精度和状态约束包含之间的理想平衡。所提出的方法被证明能够为多个足式机器人提供准确的状态估计，包括高度动态的跳跃机器人 PogoX、双足机器人 Cassie 和四足机器人 Unitree Go1，频率为 200 Hz，窗口间隔为 0.1s。
英文摘要：
In this paper, we present a fast and decentralized state estimation framework for the control of legged locomotion. The nonlinear estimation of the floating base states is decentralized to an orientation estimation via Extended Kalman Filter (EKF) and a linear velocity estimation via Moving Horizon Estimation (MHE). The EKF fuses the inertia sensor with vision to estimate the floating base orientation. The MHE uses the estimated orientation with all the sensors within a time window in the past to estimate the linear velocities based on a time-varying linear dynamics formulation of the interested states with state constraints. More importantly, a marginalization method based on the optimization structure of the full information filter (FIF) is proposed to convert the equality-constrained FIF to an equivalent MHE. This decoupling of state estimation promotes the desired balance of computation efficiency, accuracy of estimation, and the inclusion of state constraints. The proposed method is shown to be capable of providing accurate state estimation to several legged robots, including the highly dynamic hopping robot PogoX, the bipedal robot Cassie, and the quadrupedal robot Unitree Go1, with a frequency at 200 Hz and a window interval of 0.1s.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
