Exosense: A Vision-Based Scene Understanding System For Exoskeletons

Jianeng Wang¹, Matias Mattamala¹, Christina Kassab¹, Guillaume Burger², Fabio Elnecave², Lintong Zhang¹, Marine Petriaux², Maurice Fallon¹

¹ Dynamic Robot Systems Group, Oxford Robotics Institute, University of Oxford
² Wandercraft SAS

Accepted to IEEE Robotics and Automation Letters (RA-L) 2025

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Abstract: 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.

Citation

@ARTICLE{Wang2025,
  author={Wang, Jianeng and Mattamala, Matias and Kassab, Christina and Burger, Guillaume and Elnecave, Fabio and Zhang, Lintong and Petriaux, Marine and Fallon, Maurice},
  journal={IEEE Robotics and Automation Letters}, 
  title={Exosense: A Vision-Based Scene Understanding System for Exoskeletons}, 
  year={2025},
  volume={10},
  number={4},
  pages={3510-3517},
  doi={10.1109/LRA.2025.3543971}
}

Acknowledgement: This work is supported by a Royal Society University Research Fellowship (Fallon, Kassab), Horizon Europe project DigiForest 101070405 (Wang), and EPSRC C2C Grant EP/Z531212/1 (Mattamala). We thank Wayne Tubby and Matthew Graham for hardware design support.