Vision-based pose estimation is an important application of machine vision. Currently, analytical and iterative methods are used to solve the object pose. The analytical solutions generally take less computation time. However, the analytical solutions are extremely susceptible to noise. The iterative solutions minimize the distance error between feature points based on 2D image pixel coordinates. However, the non-linear optimization needs a good initial estimate of the true solution, otherwise they are more time consuming than analytical solutions. Moreover, the image processing error grows rapidly with measurement range increase. This leads to pose estimation errors. All the reasons mentioned above will cause accuracy to decrease. To solve this problem, a novel pose estimation method based on four coplanar points is proposed. Firstly, the coordinates of feature points are determined according to the linear constraints formed by the four points. The initial coordinates of feature points acquired through the linear method are then optimized through an iterative method. Finally, the coordinate system of object motion is established and a method is introduced to solve the object pose. The growing image processing error causes pose estimation errors the measurement range increases. Through the coordinate system, the pose estimation errors could be decreased. The proposed method is compared with two other existing methods through experiments. Experimental results demonstrate that the proposed method works efficiently and stably.
Keywords: analytical and iterative; four coplanar points; linear constraints; pose estimation; the coordinate system of object motion.