A new technique for three-dimensional (3D) camera calibration for machine vision metrology using off-the-shelf TV cameras and lenses is described. The two-stage technique is aimed at efficient computation of camera external position and orientation relative to object reference coordinate system as well as the effective focal length, radial lens distortion, and image scanning parameters. The two-stage technique has advantage in terms of accuracy, speed, and versatility over existing state of the art. A critical review of the state of the art is given in the beginning. A theoretical framework is established, supported by comprehensive proof in five appendixes, and may pave the way for future research on 3D robotics vision. Test results using real data are described. Both accuracy and speed are reported. The experimental results are analyzed and compared with theoretical prediction. Recent effort indicates that with slight modification, the two-stage calibration can be done in real time. IEEE JOURNAL OF ROBOTICS AND AUTOMATION, VOL. RA-3, NO. 4, AUGUST 1987 implement. ' The advantages of using off-the-shelf solid state or vidicon camera and lens are versatile-solid state cameras and lenses can be used for a variety of automation applications; availability-since off-the-shelf solid state cameras and lenses are common in many applications, they are at hand when you need them and need not be custom ordered; familiarity, user-friendly-not many people have the experience of operating the professional metric camera used in photogrammetry or the tetralateral photodiode with preamplifier and associated electronics calibration, while solid state is easily interfaced with a computer and easy to install. The next section shows deficiencies of existing techniques in one or more of these criteria. B. Why Existing Techniques Need Improvement In this section, existing techniques are first classified into several categories. The strength and weakness of each category are analyzed.

This paper describes a new technique for computing 3D position and orientation of a camera relative to the last joint of a robot manipulator in an eye-on-hand configuration. This is part of a trio for real-time 3D robotics eye, eye-to-hand, and hand calibrations, which use a common setup and calibration object, common coordinate systems, matrices, vectors, symbols, and operations throughout the trio, and is especially suited to machine vision community. It is easier and faster than any of the existing techniques, and is ten times more accurate in rotation than any existing technique using standard resolution cameras, and equal to the state-of-the-art vision based technique in terms of linear accuracy. The robot makes a series of automatically planned movements with a camera rigidly mounted at the gripper. At the end of each move, it takes a total of 90 ms to grab an image, extract image feature coordinates, and perform camera extrinsic calibration. After the robot finishes all the movements, it takes only a few milliseconds to do the calibration. A series of generic geometric properties or lemmas are presented, leading to the derivation of the final algorithms, which are aimed at simplicity, efficiency, and accuracy while giving ample geometric and algebraic insights. Besides describing the new technique, critical factors influencing the accuracy are analyzed, and procedures for improving accuracy are introduced. Test results of both simulation and real experiments on an IBM Cartesian robot are reported and analyzed. 1) Camera Calibration (see [6], [ 101, [ 1 11, [ 131). 2) Robot Eye-to-Hand Calibration (this paper). 3) Cartesian Robot Hand Calibration [5].

Motion estimation. Dynamic scene analysis. Optical flow.analysis.Image processing. Image sequence ABSTRACT (C o ntin ue on ravaraa aide i t nacaaaary and id e n tify by block number)The processing of image sequences for efficient encoding, enhancement, and dynamic scene analysis has become increasingly important. A key issue in image sequence processing is motion estimation. Past work has concentrated on the approach of solving nonlinear equations iteratively, which is unsatisfactory because of convergence and uniqueness problems. In this report we prove some important theorems on uniqueness and present a totally new motion estimation DD y 1473Unclassified Unclassified SECURITY CLASSIFICATION OF THIS PAGgfH7»«i Dmt* E n f t t i )algorithm which does not require the iterative solution of nonlinear equations.We show that seven point correspondences are sufficient to uniquely deter mine from two time-sequential views the three-dimensional motion parameters (within a scale factor for the translations) of a rigid object with curved surfaces. The seven points should not be traversed by two planes with one plane containing the origin, nor by a cone containing the origin. A set of "essential paramenters" are introduced which uniquely determine the motion parameters up to a scale factor for the translations, and can be estimated by solving a set of eight linear equations which are derived from the correspondenc of eight image points. The actual motion parameters can subsequently be deter mined by computing the singular value decomposition (SVD) of a 3x3 matrix containing the essential parameters. No nonlinear equations need be solved. Unclassified S E C U R IT Y C i-A S S m C A T lO H O F THIS

A new method for moving image restoration and registration is established. The observations are sequences of low-resolution, possibly undersampled, discrete frames. The result is a restored highresoLution image. The restoration part is attractive for the purpose of real time implementation since the computation consists of only a few complex operations per pel of the resultant high resolution image. Tests on a class of waveforms with different bandwidths show that the performance is superior to that of the cubic spline technique in terms of the signal to noise ratio. The registration part has two new features: 1) The relative shifts of several frames can be estimated in One process.2) The estimate is accruate even if the observation frames are severely undersampled, so long as the number of frames available is big enough.