A Sliding-Window Filter for Online Continuous-Time Continuum Robot State Estimation
For researchers working on continuum robot state estimation, this method offers a principled way to balance accuracy and computational efficiency online, though it is an incremental improvement over existing continuous-time techniques.
This work presents a sliding-window filter for continuous-time state estimation of continuum robots that improves accuracy over existing filter approaches while enabling online operation at faster-than-real-time speeds. It is the first stochastic sliding-window filter specifically designed for continuum robots.
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.