A new method for uncalibrated visual servoing

The paper introduces a novel method for visual servoing based on the generalization of quasi-Newton methods for nonlinear optimization. The method calibrates a linear model based on several previous iterates. The difference with existing approaches is that we do not impose the linear model to interpolate the function. Instead, we prefer to identify the linear model which is as close as possible to the nonlinear function, in the least squares sense. The new system has shown to be less sensitive to noise and exhibits a faster convergence than conventional quasi-Newton methods. The theoretical results are verified experimentally.