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。DVS的执行方式使得传统基于特征的视觉伺服流程中的几何基元提取、匹配和跟踪步骤得以省略。尽管DVS能够实现高精度定位，但由于待最小化代价函数存在极端非线性以及视觉特征间存在冗余数据，该方法面临收敛域有限和鲁棒性不足的问题。为解决上述问题，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Tchebichef矩、Krawtchouk矩和Hahn矩为例，我们不仅展示了视觉特征参数与阶数的自适应调节策略，还给出了关联交互矩阵的解析表达式。仿真实验证明了本方法的鲁棒性和准确性，以及相较于现有技术的优越性。此外，通过真实世界实验验证了本方法的有效性。