Improved Pose Estimation of Aruco Tags Using a Novel 3D Placement Strategy
Petr Oščádal, Dominik Heczko, Aleš Vysocký, Jakub Mlotek, Petr Novák, Ivan Virgala, Marek Sukop, Zdenko Bobovský
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.
Topics & Concepts
PoseComputer scienceArtificial intelligenceComputer visionRGB color modelMonocular3D pose estimationSet (abstract data type)CalibrationMathematicsProgramming languageStatisticsRobotics and Sensor-Based LocalizationAdvanced Vision and ImagingOptical measurement and interference techniques