用手机手表的加速度计和气压计实时追踪人体动作，支持不平地面。
BaroPoser: Real-time Human Motion Tracking from IMUs and Barometers in Everyday Devices
- 融合手机与手表的加速度计和气压计数据，实时估计姿态与全局位移。
- 在非平坦地形上，定位精度显著优于仅用加速度计的现有方法。
- 适合可穿戴设备上的实时动作捕捉，尤其适用于户外复杂场景。
近年来，利用智能手机和智能手表中的惯性测量单元（IMU）追踪人体运动越来越流行。然而，由于传感器数据稀疏且缺乏覆盖不平坦地形的真实运动数据集，现有方法在姿态估计精度上表现不佳，通常仅限于平坦地形。为此，我们提出BaroPoser，首个结合智能手机和智能手表的IMU与气压计数据，实现实时人体姿态与全局位移估计的方法。通过气压读数估计传感器高度变化，为提升姿态估计精度和预测非平坦地形上的全局位移提供关键线索。此外，我们引入局部大腿坐标系，解耦局部与全局运动输入，以优化姿态表征学习。我们在公开基准数据集和真实世界录制数据上评估该方法。定量与定性结果均表明，在相同硬件配置下，本方法显著优于仅使用IMU的最先进方法。
原文摘要 · Abstract (English)
In recent years, tracking human motion using IMUs from everyday devices such as smartphones and smartwatches has gained increasing popularity. However, due to the sparsity of sensor measurements and the lack of datasets capturing human motion over uneven terrain, existing methods often struggle with pose estimation accuracy and are typically limited to recovering movements on flat terrain only. To this end, we present BaroPoser, the first method that combines IMU and barometric data recorded by a smartphone and a smartwatch to estimate human pose and global translation in real time. By leveraging barometric readings, we estimate sensor height changes, which provide valuable cues for both improving the accuracy of human pose estimation and predicting global translation on non-flat terrain. Furthermore, we propose a local thigh coordinate frame to disentangle local and global motion input for better pose representation learning. We evaluate our method on both public benchmark datasets and real-world recordings. Quantitative and qualitative results demonstrate that our approach outperforms the state-of-the-art (SOTA) methods that use IMUs only with the same hardware configuration.
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