Visual servoing method using camera self-calibration

Visual servoing is very useful for navigating robots to specific positions and orientations accurately and reliably. Unfortunately, the existing methods require accurate camera calibration tasks that are troublesome. Whenever a camera is changed or intrinsic parameters become different, we should carry out camera calibration tasks. Methods in order to cope with this problem is so called self-calibration techniques. Until now, self-calibration techniques just have considered the case of static self-calibration where these estimated intrinsic parameters are not used to control the robot. Only a few researchers have recently developed the case of dynamic self-calibration where estimated intrinsic parameters are used to control the robot. In this paper, a visual servoing procedure which is performed in parallel to a self-calibration algorithm based on simplified kruppa equations is proposed. Simulation results show that estimation of both constant and varying intrinsic parameters is efficient and the the robot converge to the desired positions.