We introduce (HPS) Human POSEitioning System, a method to recover the full 3D pose of a human registered with a 3D scan of the surrounding environment using wearable sensors. Using IMUs attached at the body limbs and a head mounted camera looking outwards, HPS fuses camera based self-localization with IMU-based human body tracking. The former provides drift-free but noisy position and orientation estimates while the latter is accurate in the short-term but subject to drift over longer periods of time. We show that our optimization-based integration exploits the benefits of the two, resulting in pose accuracy free of drift. Furthermore, we integrate 3D scene constraints into our optimization, such as foot contact with the ground, resulting in physically plausible motion. HPS complements more common third-person-based 3D pose estimation methods. It allows capturing larger recording volumes and longer periods of motion, and could be used for VR/AR applications where humans interact with the scene without requiring direct line of sight with an external camera, or to train agents that navigate and interact with the environment based on first-person visual input, like real humans. With HPS, we recorded a dataset of humans interacting with large 3D scenes (300-1000 sq.m) consisting of 7 subjects and more than 3 hours of diverse motion. The dataset, code and video will be available on the project page: http://virtualhumans.mpi-inf.mpg.de/hps/ .

本文介绍了一种使用可穿戴传感器来在与周围环境的3D扫描注册的人体中恢复完整的3D姿势的方法，该方法使用身体部位处的IMU和向外看的头戴式摄像机将基于相机的自定位与基于IMU的人体跟踪融合，并将3D场景约束集成到优化中，以获得无漂移的姿态精度，从而可以在更大的记录体积和更长的运动周期中捕获3D姿势，可用于虚拟现实/增强现实应用程序或通过第一人称视觉输入训练导航和与环境交互的代理。

基于身体传感器的大场景下的 3D 人体姿态估计与自定位系统