While camera-based capture systems remain the gold standard for recording human motion, learning-based tracking systems based on sparse wearable sensors are gaining popularity. Most commonly, they use inertial sensors, whose propensity for drift and jitter have so far limited tracking accuracy. In this paper, we propose Ultra Inertial Poser, a novel 3D full body pose estimation method that constrains drift and jitter in inertial tracking via inter-sensor distances. We estimate these distances across sparse sensor setups using a lightweight embedded tracker that augments inexpensive off-the-shelf 6D inertial measurement units with ultra-wideband radio-based ranging$-$dynamically and without the need for stationary reference anchors. Our method then fuses these inter-sensor distances with the 3D states estimated from each sensor Our graph-based machine learning model processes the 3D states and distances to estimate a person's 3D full body pose and translation. To train our model, we synthesize inertial measurements and distance estimates from the motion capture database AMASS. For evaluation, we contribute a novel motion dataset of 10 participants who performed 25 motion types, captured by 6 wearable IMU+UWB trackers and an optical motion capture system, totaling 200 minutes of synchronized sensor data (UIP-DB). Our extensive experiments show state-of-the-art performance for our method over PIP and TIP, reducing position error from $13.62$ to $10.65cm$ ($22\%$ better) and lowering jitter from $1.56$ to $0.055km/s^3$ (a reduction of $97\%$).

通过利用惯性传感器，记录人体动作的摄像机捕捉系统以稳定为标准，但基于稀疏可穿戴传感器的基于学习的追踪系统越来越受欢迎。文章提出了一种新颖的三维全身姿势估计方法，通过用于测距的超宽带无线电技术对传感器之间的距离进行约束，从而限制了惯性追踪中的漂移和抖动。我们的方法通过轻量级嵌入式追踪器，将这些传感器之间的距离与每个传感器估计的三维状态进行融合，并使用基于图的机器学习模型处理这些三维状态和距离，从而估计人体的三维全身姿势和平移。实验证明，我们的方法在位置误差和抖动方面具有领先的性能。

超惯性姿态：基于稀疏惯性传感器和超宽频测距的可扩展动作捕捉和跟踪