Over the past decade, image moments have been exploited in several visual servoing schemes for their ability to represent object regions, objects defined by contours or a set of discrete points. Moments have also been useful to achieve control decoupling properties and to choose a minimal number of features to control the whole degrees of freedom (DOFs) of a camera. However, the choice of moment-based features to control the rotational motions around the x-axis and y-axis simultaneously with the translational motions along the same axis remains a key issue. In this paper, we introduce new visual features computed from low-order “shifted moments invariant.” Importantly, they allow us 1) to define a unique combination of visual features to control the whole six DOFs of an eye-in-hand camera independently from the object shape and (2) to significantly enlarge the convergence domain of the closed-loop system.

In image-based visual servoing, the choice of visual features has a strong influence on the performance of the control system. In the last decade, image-moments have been exploited in several visual servoing schemes for their ability to represent object region, object defined by contours or a set of discrete points. Despite the many recent advances, the choice of moment-based features to control the most critical Degrees Of Freedom (DOF), i.e those used to control the rotational motions around the x-axis and y-axis of the camera remains a key issue. In this paper, a new feature formula to control these critical DOF that does not depend on the object shape is proposed. The new features are computed from shifted moments and selected in order to provide nice invariant properties. Additionally, low order shifted moments can be exploited to minimize the impact of measurement noise on the control performances.