Existing model-based monocular pose measurement methods need the accurate 3D model of the target. They cannot handle the absence of the accurate 3D model. To solve this problem, this paper iteratively optimizes the 3D model of the target platform and pose , along with proposing a novel monocular vision measurement methodology. We propose to represent the target’s 3D model using a set of sparse 3D landmarks. The sparse 3D landmark and the 6D pose are parameters to be solved, and the objective function is built based on the minimization of the object-space collinearity error. By solving the optimization problem, the sparse 3D landmark and pose are iteratively optimized, and the sliding window combined with keyframe extraction strategy is adopted to achieve real-time and online high-precision monocular vision measurement. The experimental results show that the proposed method achieves efficient and effective monocular pose measurement with the absence of the accurate 3D model, and improves the accuracy of the target’s 3D model via iterative optimization of the sparse 3D landmarks and pose.