A Sliding-Window Filter for Online Continuous-Time Continuum Robot State Estimation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910267123695616 |
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| author | Teetaert, Spencer Lilge, Sven Burgner-Kahrs, Jessica Barfoot, Timothy D. |
| author_facet | Teetaert, Spencer Lilge, Sven Burgner-Kahrs, Jessica Barfoot, Timothy D. |
| contents | Stochastic state estimation methods for continuum robots (CRs) often struggle to balance accuracy and computational efficiency. While several recent works have explored sliding-window formulations for CRs, these methods are limited to simplified, discrete-time approximations and do not provide stochastic representations. In contrast, current stochastic filter methods must run at the speed of measurements, limiting their full potential. Recent works in continuous-time estimation techniques for CRs show a principled approach to addressing this runtime constraint, but are currently restricted to offline operation. In this work, we present a sliding-window filter (SWF) for continuous-time state estimation of CRs that improves upon the accuracy of a filter approach while enabling continuous-time methods to operate online, all while running at faster-than-real-time speeds. This represents the first stochastic SWF specifically designed for CRs, providing a promising direction for future research in this area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26623 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Sliding-Window Filter for Online Continuous-Time Continuum Robot State Estimation Teetaert, Spencer Lilge, Sven Burgner-Kahrs, Jessica Barfoot, Timothy D. Robotics Stochastic state estimation methods for continuum robots (CRs) often struggle to balance accuracy and computational efficiency. While several recent works have explored sliding-window formulations for CRs, these methods are limited to simplified, discrete-time approximations and do not provide stochastic representations. In contrast, current stochastic filter methods must run at the speed of measurements, limiting their full potential. Recent works in continuous-time estimation techniques for CRs show a principled approach to addressing this runtime constraint, but are currently restricted to offline operation. In this work, we present a sliding-window filter (SWF) for continuous-time state estimation of CRs that improves upon the accuracy of a filter approach while enabling continuous-time methods to operate online, all while running at faster-than-real-time speeds. This represents the first stochastic SWF specifically designed for CRs, providing a promising direction for future research in this area. |
| title | A Sliding-Window Filter for Online Continuous-Time Continuum Robot State Estimation |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.26623 |