To address the problem that the pose measurement system will increase the weight of the measured object, a visual pose measuring system based on grid-structured light is developed. First, grid-structured light is used to obtain the 3D point cloud on the surface of an object. A grid-structured-light calibration algorithm based on a 2D target is proposed. Based on the coplanarity of the target and the structured light, the grid-structured light and a camera can be calibrated simultaneously. To verify the accuracy of the point cloud obtained by the grid-structured light, structured-light calibration and point-cloud accuracy measuring experiments are conducted. The results show that the point cloud accuracy is above 0.04 mm. Moreover, the point-cloud registration algorithm for pose measurement is studied. The sample consensus initial alignment (SAC-IA) algorithm is used for coarse registration because it provides a good initial position for fine point-cloud registration. The improved iterative closet point (ICP) algorithm is used for fine registration and random sample consensus (RANSAC) algorithm and a normal vector threshold are introduced to filter matching points. The distance from the point to the surface is used as the error function to perform pose measurements. Registration algorithm comparison and pose measurement experiments are conducted. The results prove that when the measured object is in rotational motion, the maximum error of the system does not exceed 0.25° and in translational motion, the maximum error does not exceed 0.35 mm.