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Consistent State Estimation on Manifolds for Autonomous Metal Structure Inspection

Abstract : This work presents the Manifold Invariant Extended Kalman Filter, a novel approach for better consistency and accuracy in state estimation on manifolds. The robustness of this filter allows for techniques with high noise potential like ultra-wideband localization to be used for a wider variety of applications like autonomous metal structure inspection. The filter is derived and its performance is evaluated by testing it on two different manifolds: a cylindrical one and a bivariate b-spline representation of a real vessel surface, showing its flexibility to being used on different types of surfaces. Its comparison with a standard EKF that uses virtual, noise-free measurements as manifold constraints proves that it outperforms standard approaches in consistency and accuracy. Further, an experiment using a real magnetic crawler robot on a curved metal surface with ultra-wideband localization shows that the proposed approach is viable in the real world application of autonomous metal structure inspection.
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https://hal-cnrs.archives-ouvertes.fr/hal-03445976
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Submitted on : Monday, December 6, 2021 - 2:54:15 PM
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Bryan Starbuck, Alessandro Fornasier, Stephan Weiss, Cédric Pradalier. Consistent State Estimation on Manifolds for Autonomous Metal Structure Inspection. ICRA 2021, Feb 2021, virtual event, China. IEEE Computational Science and Engineering, 2021, ⟨10.1109/ICRA48506.2021.9561837⟩. ⟨hal-03445976⟩

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