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5. Improved Pose Estimation of Aruco Tags Using a Novel 3D Placement Strategy

Author

Jakub Mlotek, Petr Novák, Petr Oščádal, Marek Sukop, Zdenko Bobovský, Dominik Heczko, Ivan Virgala, and Aleš Vysocký

Subjects
0209 industrial biotechnology, Letter, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Stability (learning theory), 02 engineering and technology, lcsh:Chemical technology, pose estimation, Biochemistry, Analytical Chemistry, Set (abstract data type), 3D grid board, 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, lcsh:TP1-1185, Computer vision, Electrical and Electronic Engineering, Instrumentation, Pose, Monocular, business.industry, Object (computer science), Atomic and Molecular Physics, and Optics, Aruco, RGB color model, precision, 020201 artificial intelligence & image processing, Artificial intelligence, business, Camera resectioning
Abstract

This paper extends the topic of monocular pose estimation of an object using Aruco tags imaged by RGB cameras. The accuracy of the Open CV Camera calibration and Aruco pose estimation pipelines is tested in detail by performing standardized tests with multiple Intel Realsense D435 Cameras. Analyzing the results led to a way to significantly improve the performance of Aruco tag localization which involved designing a 3D Aruco board, which is a set of Aruco tags placed at an angle to each other, and developing a library to combine the pose data from the individual tags for both higher accuracy and stability. Web of Science 20 17 art. no. 4825

Published
2020
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