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Human and environment sensing are two important topics in Computer Vision andGraphics. Human motion is often captured by inertial sensors, while theenvironment is mostly reconstructed using cameras. We integrate the twotechniques together in EgoLocate, a system that simultaneously performs humanmotion capture (mocap), localization, and mapping in real time from sparsebody-mounted sensors, including 6 inertial measurement units (IMUs) and amonocular phone camera. On one hand, inertial mocap suffers from largetranslation drift due to the lack of the global positioning signal. EgoLocateleverages image-based simultaneous localization and mapping (SLAM) techniquesto locate the human in the reconstructed scene. On the other hand, SLAM oftenfails when the visual feature is poor. EgoLocate involves inertial mocap toprovide a strong prior for the camera motion. Experiments show thatlocalization, a key challenge for both two fields, is largely improved by ourtechnique, compared with the state of the art of the two fields. Our codes areavailable for research at https://xinyu-yi.github.io/EgoLocate/.

EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors