A self-calibration technique for active vision systems

Many vision research groups have developed the active vision platform whereby the camera motion can be controlled. A similar setup is the wrist-mounted camera for a robot manipulator. This head-eye (or hand-eye) setup considerably facilitates motion stereo, object tracking, and active perception. One of the important issues in using the active vision system is to determine the camera position and orientation relative to the camera platform. This problem is called the head-eye calibration in active vision, and the hand-eye calibration in robotics. In this paper we present a new technique for calibrating the head-eye (or hand-eye) geometry as well as the camera intrinsic parameters. The technique allows camera self-calibration because it requires no reference object and directly uses the images of the environment. Camera self-calibration is important especially in circumstances where the execution of the underlying visual tasks does not permit the use of reference objects. Our method exploits the flexibility of the active vision system, and bases camera calibration on a sequence of specially designed motion. It is shown that if the camera intrinsic parameters are known a priori, the orientation of the camera relative to the platform can be solved using 3 pure translational motions. If the intrinsic parameters are unknown, then two sequences of motion, each consisting of three orthogonal translations, are necessary to determine the camera orientation and intrinsic parameters. Once the camera orientation and intrinsic parameters are determined, the position of the camera relative to the platform can be computed from an arbitrary nontranslational motion of the platform. All the computations in our method are linear. Experimental results with real images are presented in this paper.