Work

Consistent State Estimation on Manifolds for Autonomous Metal Structure Inspection

Year: 2021

Type: conference-paper

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.

Source: Proceedings - IEEE International Conference on Robotics and Automation/Proceedings

DOI: 10.1109/icra48506.2021.9561837

Authors: Bryan Starbuck, Alessandro Fornasier, Stephan Weiss, Cedric Pradalier

Institutions: Georgia Tech-Europe, Centre National de la Recherche Scientifique, Georgia Tech-CNRS Laboratory, University of Klagenfurt

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Horizon 2020 Framework Programme

Award: G3008394639