This paper proposes a new approach to achieve direct visual servoing (DVS) based on discrete orthogonal moments (DOM).DVS is performed in such a way that the extraction of geometric primitives, matching, and tracking steps in the conventional feature-based visual servoing pipeline can be bypassed. Although DVS enables highly precise positioning, it suffers from a limited convergence domain and poor robustness due to the extreme nonlinearity of the cost function to be minimized and the presence of redundant data between visual features. To tackle these issues, we propose a generic and augmented framework that considers DOM as visual features. By using the Tchebichef, Krawtchouk, and Hahn moments as examples, we not only present the strategies for adaptively tuning the parameters and order of the visual features, but also exhibit an analytical formulation of the associated interaction matrix. Simulations demonstrate the robustness and accuracy of our approach, as well as its advantages over the state-of-the-art. Real-world experiments have also been performed to validate the effectiveness of our approach.

翻译：本文提出了一种基于离散正交矩实现直接视觉伺服的新方法。直接视觉伺服以绕过传统基于特征的视觉伺服流水线中几何基元提取、匹配和跟踪步骤的方式运行。尽管直接视觉伺服能够实现高精度定位，但由于待最小化代价函数的极端非线性以及视觉特征间存在冗余数据，其存在收敛域有限、鲁棒性差的问题。为解决这些问题，我们提出一个将离散正交矩作为视觉特征的通用增强框架。以切比雪夫矩、克劳特丘克矩和哈恩矩为例，我们不仅提出了自适应调节视觉特征参数与阶次的策略，还给出了关联交互矩阵的解析表达式。仿真实验证明了该方法的鲁棒性、精确性以及相较于现有技术的优势。同时通过真实世界实验验证了该方法的有效性。