Visual Servoing by Optimization of a 2D/3D Hybrid Objective Function

In this paper, we present a new hybrid visual servoing algorithm for robot arm positioning task. Hybrid methods in visual servoing partially combine the 2D and 3D visual information to improve the performance of the traditional image-based and position-based visual servoing. Our algorithm is superior to the state of the art hybrid methods. The objective function has been designed to include the full 2D and 3D information available either from the CAD model or from the partial reconstruction process by decomposing the homography matrix between two views. Here, each of 2D and 3D error functions is used to control the six degrees of freedom. We call this method 5D visual servoing. The positioning task has been formulated as a minimization problem. Gradient decent as a first order approximation and Gauss-Newton as a second order approximation are considered in this paper. Simulation results show that these two methods provide an efficient solution to the camera retreat and features visibility problems. The camera trajectory in the Cartesian space is also shown to be satisfactory.