Simultaneous hand-eye calibration and reconstruction

Abstract

Hand-eye calibration is a well-known calibration problem. The problem assumes that a camera (eye) is rigidly mounted to the gripper (hand) of a robot arm and aims to find the transformation between them. In this paper, we propose a novel pipeline for hand-eye calibration without the use of a calibration target. First we employ feature extraction and matching, followed by an initial hand-eye calibration step using 2-view matches. In an iterative process, we then alternately employ triangulation and bundle adjustment to optimize the reconstruction and the hand-eye calibration result. Unlike in structure from motion and traditional hand-eye calibration, during this process we always determine the global camera poses using the hand poses and the estimated hand-eye transformation. Synthetic-data and real-data experiments are performed to evaluate the proposed approach, and the results indicate that the accuracy of our approach is superior to state-of-the-art approaches. Moreover, the speed of our algorithm is faster than existing methods.