Reconstruction-Based Hand–Eye Calibration Using Arbitrary Objects

Abstract

This paper introduces a flexible hand-eye calibration technique for 3D sensor from reconstruction perspective, with no need for specialized and accurate calibration rig. Our intention is to find the hand-eye relation which simultaneously aligns multi-view point clouds of a common scene into the robot base frame, namely simultaneous calibration and reconstruction. To achieve this goal, a novel variant of iterative closest point (ICP) algorithm based on Gauss-Newton method and Lie algebra is proposed, which iteratively transforms multi-view point clouds into robot base frame, estimates point-to-point correspondences between point clouds then refines the hand-eye relation to minimize the Euclidean distance between corresponding points. In addition to the calibration result, it returns a preliminary reconstruction as byproduct. Cases of degeneracy and applicable condition are given and proved. Using arbitrary daily objects with no prior information and real robotic eye-in-hand system, we verify our method feasible and effective.