Robust Hand—Eye Calibration With a Single Plane for 3-D Robot Measurement

Robots equipped with 2-D laser profile scanners are widely utilized in industrial manufacturing processes. The hand—eye calibration is a critical aspect of robot 3-D measurement and significantly influences the accuracy of the measured point cloud. This article introduces a robust hand—eye calibration method that employs a single planar artifact in three steps. First, the normal vector of the calibration plane is determined by utilizing points gathered through translation motions. Second, the rotation part is determined by the fact that the regression line of a single scan taken on a planar surface is orthogonal to the normal vector of the calibration plane. The translation vector is ultimately determined by leveraging the property that the line connecting the centers of two scans is perpendicular to the plane’s normal vector. The objective function is formulated using the principal component analysis (PCA) technique and the Kronecker product. We propose a dual-constraint rotation objective function to enhance the robustness of the method against measurement noise. A closed-form solution is derived by employing the least-squares method. The proposed methodology can directly calculate the accurate hand—eye matrix and exhibits a favorable tolerance for measurement noise originating from the scanner and deviations in robot pose. The effectiveness of the proposed algorithm has been confirmed through validation using both simulated data and real-world experiments.