Exosense: A Vision-Based Scene Understanding System For Exoskeletons
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916557386416128 |
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| author | Wang, Jianeng Mattamala, Matias Kassab, Christina Burger, Guillaume Elnecave, Fabio Zhang, Lintong Petriaux, Marine Fallon, Maurice |
| author_facet | Wang, Jianeng Mattamala, Matias Kassab, Christina Burger, Guillaume Elnecave, Fabio Zhang, Lintong Petriaux, Marine Fallon, Maurice |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14320 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exosense: A Vision-Based Scene Understanding System For Exoskeletons Wang, Jianeng Mattamala, Matias Kassab, Christina Burger, Guillaume Elnecave, Fabio Zhang, Lintong Petriaux, Marine Fallon, Maurice Robotics Computer Vision and Pattern Recognition 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. |
| title | Exosense: A Vision-Based Scene Understanding System For Exoskeletons |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.14320 |