An Uncalibrated Visual Servo Method Based on Projective Homography

Abstract

An uncalibrated visual servo method based on projective homography, denoted as Projective Homography based Uncalibrated Visual Servoing (PHUVS), is proposed in this paper, in which a novel task function based on the element of projective homography is devised to realize visual servo without a prior knowledge of the camera intrinsic parameters and hand-eye relationships. The main advantage of this method is that it is not only suitable for totally uncalibrated scenarios but also cheap in computation costs when compared with classical image-based uncalibrated visual servoing methods. Numerical experiments are performed and the results confirm that the new approach is capable of both static positioning and dynamic tracking tasks, and presents competitive computational efficiency and accuracy performance. Note to Practitioners —Vision guided robot has been widely used in industries. However the industrial state-of-art robot visual servo systems usually require system calibration, including camera calibration, hand-eye calibration, and robot calibration. These calibration process are time consuming and requires some expertise, which is rare for ordinary workers. Moreover, when hand-eye configuration changed due to certain reasons such as adjustment of production line layout or displacement due to machining force or switch of robot end effector, the calibrated parameters are no longer accurate. This paper offers a plug and play approach for eye-in-hand robot visual servo tasks. When a robot-camera system is set up, there is no need for the operator to calibrate the system. With reference (goal) image and the current images obtained by the moving camera, the robot can autonomously achieve the goal pose which is described simply by the reference camera. With our novel task function, the computation cost is reduced, which will benefit the real-time application. The accuracy and some dynamic performance in both static positioning and dynamic target tracking are evaluated by robotic toolbox. This approach has the potential to improve the flexibility and robustness of robot system in robotic manipulation tasks such as assemble and painting, or robotic machining tasks such as matching the origin point of operating trajectory in large part manufacturing. Furthermore, this approach is generic, and can be extended to mobile robot visual servo control tasks.