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

TL;DR

This paper introduces a novel sliding-window filter for online continuous-time state estimation of continuum robots, balancing accuracy and computational efficiency, and enabling real-time operation.

Contribution

It presents the first stochastic sliding-window filter specifically designed for continuum robots, improving accuracy and enabling online continuous-time estimation.

Findings

Operates faster-than-real-time for continuum robots.

Provides more accurate state estimates than previous methods.

Enables online continuous-time estimation for CRs.

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.