Event
Factor Graph Combined with Magnus Prior for Continuum Robot State Estimation
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A preprint submitted by Lorenzo Ticozzi and other authors proposes a state estimation method for continuum robots: within a factor graph framework, it estimates the coefficients of a compact Geometric Variable Strain (GVS) parameterization, and uses kinematic factors derived from the Magnus expansion of the strain field to relate the GVS strain coefficients to backbone pose variables, thereby reconstructing the robot arm's shape. The authors report that, compared with a Gaussian process regression baseline on simulation and experimental data, the method achieves higher accuracy in partial and sparse measurement scenarios while maintaining computational efficiency. The state vector is compact and can be used directly for model-based control.
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- Continuum Robot State Estimation Based on Factor Graphs and Magnus Expansion Priors
This paper proposes estimating the coefficients of a compact Geometric Variable Strain (GVS) parameterization within a factor graph framework, and uses a kinematic factor derived from the Magnus expansion of the strain field to link the GVS strain coefficients with backbone pose variables, thereby reconstructing the shape of a continuum manipulator. The method is compared against a Gaussian process regression baseline on simulated and experimental data, achieving higher accuracy in partial and sparse measurement scenarios while maintaining computational efficiency. The state vector is compact and can be used directly for model-based control.
arXiv cs.RO ·