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.

Department of Mechatronics Faculty of Mechanical Engineering Technical University of Košice 04200 Košice Slovakia

Department of Robotics Faculty of Mechanical Engineering Technical University of Košice 04200 Košice Slovakia

Department of Robotics Faculty of Mechanical Engineering VSB TU Ostrava 70833 Ostrava Czech Republic

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