Hand–Eye Calibration Applied to Viewpoint Selection for Robotic Vision

Viewpoint calibration is a method to manipulate hand-eye for generating calibration parameters for active viewpoint control and object grasping. In robot vision applications, accurate vision sensor calibration and robust vision-based robot control are essential for developing an intelligent and autonomous robotic system. This paper presents a new approach to hand-eye robotic calibration for vision-based object modeling and grasping. Our method provides a 1.0-pixel level of image registration accuracy when a standard Puma/Kawasaki robot generates an arbitrary viewpoint. To attain this accuracy, our new formalism of hand-eye calibration deals with a lens distortion model of a vision sensor. Our most distinguished approach of optimizing intrinsic parameters is to utilize a new parameter estimation algorithm using an extended Kalman filter. Most previous approaches did not even consider the optimal estimates of the intrinsic and extrinsic camera parameters, or chose one of the estimates obtained from multiple solutions, which caused a large amount of estimation error in hand-eye calibration. We demonstrate the power of this new method for: (1) generating 3-D object models using an interactive 3-D modeling editor; (2) recognizing 3-D objects using stereovision systems; and (3) grasping 3-D objects using a manipulator. Experimental results using Puma and Kawasaki robots are shown.