Exosense: A Vision-Based Scene Understanding System For Exoskeletons
This addresses the need for scene perception in exoskeletons for individuals with mobility impairments, representing an incremental advancement in navigation and mapping for assistive robotics.
The authors tackled the problem of enabling self-balancing exoskeletons for daily activities by developing Exosense, a vision-based scene understanding system, which achieved an odometry drift of about 4 cm per meter traveled and terrain maps with under 1 cm average reconstruction error.
Self-balancing exoskeletons are a key enabling technology for individuals with mobility impairments. While the current challenges focus on human-compliant hardware and control, unlocking their use for daily activities requires a scene perception system. In this work, we present Exosense, a vision-centric scene understanding system for self-balancing exoskeletons. We introduce a multi-sensor visual-inertial mapping device as well as a navigation stack for state estimation, terrain mapping and long-term operation. We tested Exosense attached to both a human leg and Wandercraft's Personal Exoskeleton in real-world indoor scenarios. This enabled us to test the system during typical periodic walking gaits, as well as future uses in multi-story environments. We demonstrate that Exosense can achieve an odometry drift of about 4 cm per meter traveled, and construct terrain maps under 1 cm average reconstruction error. It can also work in a visual localization mode in a previously mapped environment, providing a step towards long-term operation of exoskeletons.