An overview of hand-eye calibration

Abstract

Due to the increase in the difficulty and diversity of tasks performed by robots, robot "hand-eye" collaborative operation has attracted widespread attention. This technology is widely used in aerospace, medical, automotive, and industrial fields. Recently, hand-eye calibration technology is developing towards high precision and high intelligence. However, it has much work to be done in terms of identifying robot and camera parameters. This article introduces in detail the methods and theories involved in hand-eye calibration. According to the structure of the algorithm and the type of the optimization method, this paper summarizes the hand-eye calibration method into four steps: camera pose, mechanical claw pose, mathematical model, and error metrics. The well-known open problems about hand-eye calibration are finally stated, and some new research interests are also pointed out. The results of this review are useful for robot technicians to choose the correct parameter identification method and for researchers to determine further research areas.

A Deep Learning-Based Hand-eye Calibration Approach using a Single Reference Point on a Robot Manipulator

“…Hand-eye calibration involves finding the geometric transformation between the sensor and the robot to transfer camera measurements into the robot workspace. In the literature, the hand-eye calibration problem is solved by collecting several cameras poses and the corresponding robot's end-effector poses which are then passed to a handeye calibration algorithm that estimates the transformation between the camera and the robot [1].…”

“…The main limitation of offline hand-eye calibration approaches is that they are not flexible against camera pose changes with respect to the robot base after obtaining the geometric transformation model between the camera and the robot's world coordinate frame. To overcome this problem, online hand-eye calibration approaches [2] [3] have been developed over the last 1 School of Computing Science, University of Glasgow, G12 8QQ, Scotland, United Kingdom; {Ozan.Bahadir}, {Paul.Siebert}, {Gerardo.AragonCamarasa}@glasgow.ac.uk Ozan Bahadir thanks the funding received by the Turkish Ministry of National Education for this research Fig. 1.…”

A regularization-patching dual quaternion optimization method for solving the hand-eye calibration problem

“…The hand-eye calibration problem is an important part of robot calibration, which has wide applications in aerospace, medical, automotive and industrial fields [15,10]. The problem is to determine the homogeneous matrix between the robot gripper and a camera mounted rigidly on the gripper or between a robot base and the world coordinate system.…”

Calibration and error compensation of scanner-based robotic belt grinding system

“…Taking the steam turbine blade as an example, most of the operations are done in the machining center from a minimum envelope stainless steel blank except for the finishing of airfoil surfaces. Successful belt grinding involves many aspects such as coordinate system calibration, 1 grinding process modeling, 2 trajectory planning, 3–5 grinding force adaptive control, 6 error analysis, and compensation. 7,8 In order to achieve the practical application of the belt grinding system, the first step is to establish an efficient, accurate, and practical coordinate system calibration method.…”

“…9,10 Robotic belt grinding system is a typical “hand-eye” system. The traditional robot hand-eye calibration is to solve a homogeneous equation AX = XB or AX = YB , where X and Y are the unknown hand-to-camera rigid transformation and the robot-to-world rigid transformation, respectively, and A and B are the movements of the hand and camera, respectively 1 . Li et al 11 calibrated the coordinate system relationship between the robot end effector and the scanner with a calibration ball, and also gave the robot joint error in the calibration process.…”