Pose estimation is a major concern in machine vision. The iterative speed of the basic overlap pose estimation algorithm is slow. The initial point for iteration is selected as a weak perspective transformation, and the calculation result is easy to fall into the local optimal solution. Given this situation, this study proposes an improves forward overlapping pose estimation algorithm. Kronecker’s product optimizes the objective function of matter square residual, which reduces the complexity of orthogonal iterations and improves the calculation speed while ensuring iteration accuracy. The difference between the reference point and the reprojection point on the image is calculated using the idea of weighting, and different weights are given to the image square residual to reduce the effect of the error points on the results. The simulation and real experiments, when compared with the traditional normal superposition method and linear pose estimation algorithm, show that the algorithm effectively improves the calculation accuracy, speeds up the calculation speed, converges globally, and has higher practicability.