MechaTag: A Mechanical Fiducial Marker and the Detection Algorithm

Abstract

AbstractFiducial markers are fundamental components of many computer vision systems that help, through their unique features (e.g., shape, color), a fast localization of spatial objects in unstructured scenarios. They find applications in many scientific and industrial fields, such as augmented reality, human-robot interaction, and robot navigation. In order to overcome the limitations of traditional paper-printed fiducial markers (i.e. deformability of the paper surface, incompatibility with industrial and harsh environments, complexity of the shape to reproduce directly on the piece), we aim at exploiting existing, or additionally fabricated, structural features on rigid bodies (e.g., holes), developing a fiducial mechanical marker system called MechaTag. Our system, endowed with a dedicated algorithm, is able to minimize recognition errors and to improve repeatability also in case of ill boundary conditions (e.g., partial illumination). We assess MechaTag in a pilot study, achieving a robustness of fiducial marker recognition above 95% in different environment conditions and position configurations. The pilot study was conducted by guiding a robotic platform in different poses in order to experiment with a wide range of working conditions. Our results make MechaTag a reliable fiducial marker system for a wide range of robotic applications in harsh industrial environments without losing accuracy of recognition due to the shape and material.