用惯性与超宽带传感器融合，实时估计人体3D姿态与形状。
UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units
- 基于不确定度驱动的紧耦合卡尔曼滤波，融合IMU与超宽带数据
- 在真实与合成数据集上，姿态误差优于现有方法
- 适合可穿戴设备、动作捕捉等需要高精度实时估计场景
稀疏可穿戴惯性测量单元（IMUs）在三维人体运动估计中日益流行。然而，姿势歧义、数据漂移及对不同体型适应性差等问题仍存在。为此，我们提出UMotion，一种由不确定性驱动的在线融合状态估计框架，结合六个集成于身体上的超宽带（UWB）距离传感器与IMUs。UWB传感器通过测量节点间距离推断空间关系，在结合人体解剖学数据时有助于解决姿势歧义和体型差异问题。然而，IMUs易产生漂移，而UWB受身体遮挡影响。因此，我们设计了一种紧耦合无迹卡尔曼滤波（UKF）框架，融合传感器数据与基于个体体型的运动估计中的不确定性。该框架通过实时对齐传感器测量与具有不确定性的运动约束，迭代优化每一步的估计结果。在合成与真实世界数据集上的实验表明，UMotion能有效稳定传感器数据，并在姿态精度上超越现有最优方法。
原文摘要 · Abstract (English)
Sparse wearable inertial measurement units (IMUs) have gained popularity for estimating 3D human motion. However, challenges such as pose ambiguity, data drift, and limited adaptability to diverse bodies persist. To address these issues, we propose UMotion, an uncertainty-driven, online fusing-all state estimation framework for 3D human shape and pose estimation, supported by six integrated, body-worn ultra-wideband (UWB) distance sensors with IMUs. UWB sensors measure inter-node distances to infer spatial relationships, aiding in resolving pose ambiguities and body shape variations when combined with anthropometric data. Unfortunately, IMUs are prone to drift, and UWB sensors are affected by body occlusions. Consequently, we develop a tightly coupled Unscented Kalman Filter (UKF) framework that fuses uncertainties from sensor data and estimated human motion based on individual body shape. The UKF iteratively refines IMU and UWB measurements by aligning them with uncertain human motion constraints in real-time, producing optimal estimates for each. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of UMotion in stabilizing sensor data and the improvement over state of the art in pose accuracy.
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