This paper proposes a new approach to achieve direct visual servoing (DVS) based on discrete orthogonal moments (DOMs). 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 DOMs 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（DOMs）实现直接视觉伺服（DVS）的新方法。该方法能够绕过传统基于特征的视觉伺服流程中的几何基元提取、匹配与跟踪步骤。尽管DVS可实现高精度定位，但由于待最小化代价函数的极端非线性以及视觉特征间的数据冗余，其仍面临收敛域有限且鲁棒性不足的问题。针对这些挑战，我们提出了一种以DOMs为视觉特征的通用增强框架。以切比雪夫矩、克劳特楚克矩和哈恩矩为例，我们不仅展示了视觉特征参数与阶次的自适应调节策略，还给出了关联交互矩阵的解析表达式。仿真实验验证了本方法的鲁棒性与精度，并证明了其相较于现有技术的优越性。此外，真实场景实验也有效验证了本方法的可行性。