Visual Servoing with Moments of SIFT Features

Robotic manipulation of daily-life objects is an essential requirement in service robotic applications. In that context image based visual servoing is a means to position the end-effector in order to manipulate objects of unknown pose. This contribution proposes a 6 DOF visual servoing scheme that relies on the pixel coordinates, scale and orientation of SIFT features. The control is based on geometric moments computed over an alterable set of redundant SIFT feature correspondences between the current and the reference view. The method is generic as it does not depend on a geometric object model but automatically extracts SIFT features from images of the object. The foundation of visual servoing on generic SIFT features renders the method robust with respect to loss of redundant features caused by occlusion or changes in view point. The moment based representation establishes an approximate one-to-one relationship between visual features and degrees of motion. This property is exploited in the design of a decoupled controller that demonstrates superior performance in terms of convergence and robustness compared with an inverse image Jacobian controller. Several experiments with a robotic arm equipped with a monocular eye-in-hand camera demonstrate that the approach is efficient and reliable.