Exosense: a vision-based scene understanding system for exoskeletons

Journal article

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.

Authors: Wang, J; Mattamala Aravena, M; Kassab, C; Burger, G; Elnecave, F

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: IEEE Robotics and Automation Letters
• Volume: 10
• Issue: 4
• Pages: 3510 - 3517
• Publication date: 2025-02-20
• Acceptance date: 2025-01-15
• DOI: 10.1109/lra.2025.3543971
• EISSN: 2377-3766
• Language: English
• Keywords: legged locomotion; odometry; cameras; sensors; robots; exoskeletons; location awareness; visualization; semantics; robot sensing systems