A Sliding-Window Filter for Online Continuous-Time Continuum Robot State Estimation

Abstract: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.

8 pages, 6 figures. Published in the Proceedings of IEEE-RAS International Conference on Soft Robotics 2026

Robotics (cs.RO)

arXiv:2510.26623 [cs.RO]

(or arXiv:2510.26623v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2510.26623 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/RoboSoft67810.2026.11522922 Focus to learn more DOI(s) linking to related resources

142_A_Sliding_Window_Filter.mp4

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