Fiducial 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.

基准标记是许多计算机视觉系统的基本组成部分，它们通过其独特的特征（例如形状、颜色）帮助在非结构化场景中快速定位空间对象。它们在许多科学和工业领域都有应用，例如增强现实、人机交互和机器人导航。为了克服传统纸印基准标记的局限性（即纸面的变形性、与工业和恶劣环境的不兼容、直接在工件上复制的形状的复杂性），我们旨在利用现有的或额外制造的，刚体（例如孔）上的结构特征，开发了一种称为 MechaTag 的基准机械标记系统。我们的系统具有专用算法，能够最大限度地减少识别错误并提高可重复性，即使在边界条件不佳的情况下（例如，部分照明）。我们在一项试点研究中评估了 MechaTag，在不同的环境条件和位置配置下实现了 95% 以上的基准标记识别鲁棒性。试点研究是通过引导机器人平台以不同姿势进行的，以便在各种工作条件下进行实验。我们的结果使 MechaTag 成为可靠的基准标记系统，适用于恶劣工业环境中的各种机器人应用，而不会因形状和材料而损失识别精度。在不同的环境条件和位置配置下，基准标记识别的鲁棒性达到 95% 以上。试点研究是通过引导机器人平台以不同姿势进行的，以便在各种工作条件下进行实验。我们的结果使 MechaTag 成为可靠的基准标记系统，适用于恶劣工业环境中的各种机器人应用，而不会因形状和材料而损失识别精度。在不同的环境条件和位置配置下，基准标记识别的鲁棒性达到 95% 以上。试点研究是通过引导机器人平台以不同姿势进行的，以便在各种工作条件下进行实验。我们的结果使 MechaTag 成为可靠的基准标记系统，适用于恶劣工业环境中的各种机器人应用，而不会因形状和材料而损失识别精度。