  Human and environment sensing are two important topics in Computer Vision and
Graphics. Human motion is often captured by inertial sensors, while the
environment is mostly reconstructed using cameras. We integrate the two
techniques together in EgoLocate, a system that simultaneously performs human
motion capture (mocap), localization, and mapping in real time from sparse
body-mounted sensors, including 6 inertial measurement units (IMUs) and a
monocular phone camera. On one hand, inertial mocap suffers from large
translation drift due to the lack of the global positioning signal. EgoLocate
leverages image-based simultaneous localization and mapping (SLAM) techniques
to locate the human in the reconstructed scene. On the other hand, SLAM often
fails when the visual feature is poor. EgoLocate involves inertial mocap to
provide a strong prior for the camera motion. Experiments show that
localization, a key challenge for both two fields, is largely improved by our
technique, compared with the state of the art of the two fields. Our codes are
available for research at https://xinyu-yi.github.io/EgoLocate/.
