%0 Unpublished work %T Consistent State Estimation on Manifolds for Autonomous Metal Structure Inspection %+ Georgia Tech Lorraine [Metz] %A Starbuck, Bryan %A Fornasier, Alessandro %A Weiss, Stephan %A Pradalier, Cédric %< avec comité de lecture %Z ROBOTICS %@ 1070-9924 %J IEEE Computational Science and Engineering %B ICRA 2021 %C virtual event, China %I Institute of Electrical and Electronics Engineers (IEEE) %8 2021-02 %D 2021 %R 10.1109/ICRA48506.2021.9561837 %Z Engineering Sciences [physics]Special issue %X 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. %G English %2 https://hal-cnrs.archives-ouvertes.fr/hal-03445976/document %2 https://hal-cnrs.archives-ouvertes.fr/hal-03445976/file/2021%20Consistent_State_Estimation_on_Manifolds_for_Autonomous_Metal_Structure_Inspection_.pdf %L hal-03445976 %U https://hal-cnrs.archives-ouvertes.fr/hal-03445976 %~ CNRS %~ UNIV-FCOMTE %~ OPENAIRE %~ CENTRALESUPELEC %~ UMI-GTL %~ TEST-HALCNRS