Robotic Hand–Eye Calibration Method Using Arbitrary Targets Based on Refined Two-Step Registration

To optimize the structure and workflow of the 3D measurement robot system, reduce the dependence on specific calibration targets or high-precision calibration objects, and improve the versatility of the system's self-calibration, this paper proposes a robot hand-eye calibration algorithm based on irregular targets. By collecting the 3D structural information of an object in space at different positions, a random sampling consistency evaluation based on the fast point feature histogram (FPFH) is adopted, and the iterative closest point (ICP) registration algorithm with the introduction of a probability model and covariance optimization is combined to iteratively solve the spatial relationship between point clouds, and the hand-eye calibration equation group is constructed through spatial relationship analysis to solve the camera's hand-eye matrix. In the experiment, we use arbitrary objects as targets to execute the hand-eye calibration algorithm and verify the effectiveness of the method.