无需标定即可实时追踪人体关节位置，仅用一个惯性传感器。
MinJointTracker: Real-time inertial kinematic chain tracking with joint position estimation and minimal state size
- 基于递归贝叶斯估计，联合追踪全局姿态与关节位置。
- 在模拟机械臂和真人行走数据上均实现无漂移的绝对姿态估计。
- 状态量极小，适合嵌入式设备，适用于户外运动捕捉。
惯性动作捕捉是实验室外运动捕获的有前景方法。但当前多数方法需离线标定段长、IMU与肢体坐标系间的相对方向（即IMU-to-segment校准），或在IMU帧中的关节位置，导致设置繁琐。本文提出一种实时、无需标定的运动链惯性追踪算法，可同时递归估计全局IMU角运动学及IMU坐标系下的关节位置，且状态空间最小。在三段运动链（机械臂实验）和重模拟健康人行走（下肢实验）的仿真数据上，该算法仅需一个IMU，即实现无漂移的相对与绝对姿态估计，并具备全局航向参考；在不同运动场景中，关节位置估计收敛快速且鲁棒。
原文摘要 · Abstract (English)
Inertial motion capture is a promising approach for capturing motion outside the laboratory. However, as one major drawback, most of the current methods require different quantities to be calibrated or computed offline as part of the setup process, such as segment lengths, relative orientations between inertial measurement units (IMUs) and segment coordinate frames (IMU-to-segment calibrations) or the joint positions in the IMU frames. This renders the setup process inconvenient. This work contributes to real-time capable calibration-free inertial tracking of a kinematic chain, i.e. simultaneous recursive Bayesian estimation of global IMU angular kinematics and joint positions in the IMU frames, with a minimal state size. Experimental results on simulated IMU data from a three-link kinematic chain (manipulator study) as well as re-simulated IMU data from healthy humans walking (lower body study) show that the calibration-free and lightweight algorithm provides not only drift-free relative but also drift-free absolute orientation estimates with a global heading reference for only one IMU as well as robust and fast convergence of joint position estimates in the different movement scenarios.
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