The problem of coplanar reference points in monocular vision is a fundamental and critical aspect within the field of computer vision. We establish a model for the coplanar Perspective-n-Point problem and present a robust method, which comprises two main components: a direct algorithm and an iterative algorithm. In direct algorithm part, we address the problem of scale inconsistency during the pose estimation process, employ the singular value decomposition method to improve the recovery of the rotation matrix, leading to an estimation of the camera pose. This estimated pose is then used as the initial value for the iterative algorithm. In iterative algorithm part, we introduce an orthogonal iterative algorithm with the object-space collinearity error as the objective function. To enhance the robustness of this algorithm, a weighted orthogonal iterative algorithm is studied. We establish a threshold for determining outliers in the reprojection error and introduce weight information for this algorithm. Experimental results demonstrate that under conditions of limited reference points or minimal outliers, the proposed method exhibits excellent computational accuracy and robustness. It may provide significant practical value.