用惯性传感器融合动力学模型，精准估算多体运动姿态与受力。
A Tightly Coupled IMU-Based Motion Capture Approach for Estimating Multibody Kinematics and Kinetics
- 通过迭代扩展卡尔曼滤波，直接耦合IMU数据与多体动力学模型。
- 在摆锤和Kuka机械臂上，关节角度误差最大仅3.75度，扭矩误差最高3.73牛·米。
- 无需磁力计，可拓展融合光学或扭矩传感器，适合临床康复场景。
惯性测量单元（IMUs）使便携式多体运动捕捉成为可能，适用于实验室外的临床或家庭康复场景。然而，磁干扰和漂移误差限制了其广泛应用。本文提出一种紧密耦合的运动捕捉方法，通过迭代扩展卡尔曼滤波（IEKF），将IMU测量值与多体动力学模型直接融合，同时估计系统的运动学与动力学状态。该方法仅使用加速度计和陀螺仪数据，通过施加运动学与动力学约束，显著提升估计精度。可进一步融合光学运动捕捉或关节力矩数据以增强性能。我们在三自由度摆锤和六自由度Kuka机器人上验证了该方法：摆锤最大关节角均方根差（RMSD）为3.75度（相比光学运动捕捉逆运动学）；Kuka机器人最大关节角RMSD为3.24度（相比内部编码器），而光学逆运动学对比编码器的最大误差为1.16度；摆锤最大关节力矩RMSD为2牛·米（相比光学逆动力学），Kuka机器人最大力矩误差为3.73牛·米（相比内部力矩传感器）。
原文摘要 · Abstract (English)
Inertial Measurement Units (IMUs) enable portable, multibody motion capture (MoCap) in diverse environments beyond the laboratory, making them a practical choice for diagnosing mobility disorders and supporting rehabilitation in clinical or home settings. However, challenges associated with IMU measurements, including magnetic distortions and drift errors, complicate their broader use for MoCap. In this work, we propose a tightly coupled motion capture approach that directly integrates IMU measurements with multibody dynamic models via an Iterated Extended Kalman Filter (IEKF) to simultaneously estimate the system's kinematics and kinetics. By enforcing kinematic and kinetic properties and utilizing only accelerometer and gyroscope data, our method improves IMU-based state estimation accuracy. Our approach is designed to allow for incorporating additional sensor data, such as optical MoCap measurements and joint torque readings, to further enhance estimation accuracy. We validated our approach using highly accurate ground truth data from a 3 Degree of Freedom (DoF) pendulum and a 6 DoF Kuka robot. We demonstrate a maximum Root Mean Square Difference (RMSD) in the pendulum's computed joint angles of 3.75 degrees compared to optical MoCap Inverse Kinematics (IK), which serves as the gold standard in the absence of internal encoders. For the Kuka robot, we observe a maximum joint angle RMSD of 3.24 degrees compared to the Kuka's internal encoders, while the maximum joint angle RMSD of the optical MoCap IK compared to the encoders was 1.16 degrees. Additionally, we report a maximum joint torque RMSD of 2 Nm in the pendulum compared to optical MoCap Inverse Dynamics (ID), and 3.73 Nm in the Kuka robot relative to its internal torque sensors.
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