The invention provides a novel hand-eye calibration method combining an existing hand-eye calibration method and a random sample consensus (RANSAC) algorithm. The problem of screening of calibration data can be effectively solved, and meanwhile the accuracy and robustness of the hand-eye calibration algorithm are improved. The novel hand-eye calibration method comprises the following steps of setting data screening rules; comprehensively considering the two factors of the included angle between relative movement rotation axes of a joint arm tail end, and the rotary angle difference of relative movement of a measuring arm and a scanning measuring head; carrying out threshold self-adaption pre-screening on calibration data through the RANSAC algorithm; and estimating the screened calibration data through the existing hand-eye calibration method. The solution accuracy of a rotation matrix in a hand-eye calibration equation can be improved by increasing the included angle (kAij, kAjk) between two-time relative movement rotation axes of a robot arm tail end executor and increasing the rotary angle error delta theta of relative movement of the robot arm tail end executor and a camera coordinate system.
Portable laser scanning measuring arm hand-eye calibration method based on RANSAC