A robust affine iterative closest point algorithm is proposed based on a control point consistency constraint. The proposed algorithm establishes an objective function that is constrained by the control points, and the data point set can be affinely registered to a target point set. Furthermore, the nearest point correspondence is established between the data point and target point sets; subsequently, a random sample consensus method is used to select the high-precision shape control points, and the new affine transformation is obtained using an objective function under a control point constraint. The experimental results demonstrate that the accuracy and the robustness of the proposed algorithm are improved significantly when compared with those exhibited by the conventional image point set affine registration algorithms.