In order to control the end-effector to achieve accurate and suitable posture for high-precision processes in complex industrial environments, a mechanism is often added to make secondary adjustment of the tool end posture. The 3-RPS parallel connection is widely used for target posture control in small workspaces due to its features of high control accuracy, large load, and small size and mass. In order to avoid the influence of process error and assembly error on the control accuracy of the parallel system, it is necessary to identify its parameters with high accuracy before use. However, the traditional non-contact calibration methods, such as position sensors and cameras, have the disadvantages of complex system, difficult to build, lower accuracy and cumbersome operation. To address the above problems, this paper proposes a non-contact measurement calibration method based on facial laser scanner, and establishes a calibration system based on a self-designed and fabricated 3-RPS parallel robot, realizes the high-precision measurement of the overall position and attitude information of the robot through the splicing of multi-segmented point clouds and feature extraction algorithms, establishes an inverse kinematic model of the robot, combines the 3D model with the parameter identification equations, realizes the parallel robot kinematic calibration, and designs the accuracy verification method. The effectiveness of the parameter calibration and the control algorithm of the parallel robot are verified by experiments.