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

TL;DR

EgoLocate integrates sparse body-mounted inertial sensors and a monocular camera to perform real-time human motion capture, localization, and environment mapping, improving accuracy over existing methods.

Contribution

The paper introduces a novel system that combines inertial mocap with SLAM to enhance real-time human localization and mapping using minimal sensors.

Findings

Localization accuracy is significantly improved.

System operates in real time.

Outperforms state-of-the-art methods.

Abstract

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/.