A Sliding-Window Filter for Online Continuous-Time Continuum Robot State Estimation
This work introduces a new stochastic sliding-window filter for estimating continuum robot states online. It improves accuracy over standard filtering methods while allowing continuous-time operation at faster speeds than real-time. The method uses a factor graph approach to balance estimation quality with computational efficiency for flexible robots.