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Real Time Fiducial Marker Localisation System with Full 6 DOF Pose Estimation

Real Time Fiducial Marker Localisation System with Full 6 DOF Pose Estimation The ability to reliably determine its own position, as well as the position of surrounding objects, is crucial for any autonomous robot. While this can be achieved with a certain degree of reliability, augmenting the environment with artificial markers that make these tasks easier is often practical. This applies especially to the evaluation of robotic experiments, which often require exact ground truth data containing the positions of the robots. This paper proposes a new method for estimating the position and orientation of circular fiducial markers in 3D space. Simulated and real experiments show that our method achieved three times lower localisation error than the method it derived from. The experiments also indicate that our method outperforms state-of-the-art systems in terms of orientation estimation precision while maintaining similar or better accuracy in position estimation. Moreover, our method is computationally efficient, allowing it to detect and localise several markers in a fraction of the time required by the state-of-the-art fiducial markers. Furthermore, the presented method requires only an off-the-shelf camera and printed tags, can be quickly set up and works in natural light conditions outdoors. These properties make it a viable alternative to expensive high-end localisation systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGAPP Applied Computing Review Association for Computing Machinery

Real Time Fiducial Marker Localisation System with Full 6 DOF Pose Estimation

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References (58)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2023 Copyright is held by the owner/author(s)
ISSN
1559-6915
eISSN
1931-0161
DOI
10.1145/3594264.3594266
Publisher site
See Article on Publisher Site

Abstract

The ability to reliably determine its own position, as well as the position of surrounding objects, is crucial for any autonomous robot. While this can be achieved with a certain degree of reliability, augmenting the environment with artificial markers that make these tasks easier is often practical. This applies especially to the evaluation of robotic experiments, which often require exact ground truth data containing the positions of the robots. This paper proposes a new method for estimating the position and orientation of circular fiducial markers in 3D space. Simulated and real experiments show that our method achieved three times lower localisation error than the method it derived from. The experiments also indicate that our method outperforms state-of-the-art systems in terms of orientation estimation precision while maintaining similar or better accuracy in position estimation. Moreover, our method is computationally efficient, allowing it to detect and localise several markers in a fraction of the time required by the state-of-the-art fiducial markers. Furthermore, the presented method requires only an off-the-shelf camera and printed tags, can be quickly set up and works in natural light conditions outdoors. These properties make it a viable alternative to expensive high-end localisation systems.

Journal

ACM SIGAPP Applied Computing ReviewAssociation for Computing Machinery

Published: Apr 20, 2023

Keywords: fiducial markers

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