Computationally Efficient Iterative Pose Estimation for Space Robot Based on Vision

In postestimation problem for space robot, photogrammetry has been used to determine the relative pose between an object and a camera. The calculation of the projection from two-dimensional measured data to three-dimensional models is of utmost importance in this vision-based estimation however, this process is usually time consuming, especially in the outer space environment with limited performance of hardware. This paper proposes a computationally efficient iterative algorithm for pose estimation based on vision technology. In this method, an error function is designed to estimate the object-space collinearity error, and the error is minimized iteratively for rotation matrix based on the absolute orientation information. Experimental result shows that this approach achieves comparable accuracy with the SVD-based methods; however, the computational time has been greatly reduced due to the use of the absolute orientation method. 1. Introduction Vision based methods have been applied to estimate the pose of space robot since 1990s. In these methods, the relative position and orientation between a camera and a robot target are determined with a set of feature points expressed in the three dimensional (3D) object coordinates and their two dimensional (2D) projection in the camera coordinate. The error in position and orientation is usually optimized using the noniterative or iterative algorithms. The noniterative algorithms give an analytical solution for the optimization [1–3], and a typical example of these algorithms includes the method to represent feature points as a linear combination of four virtual control points based on their coordinates [4]. The noniterative methods are generally less time consuming than the iterative methods with acceptable accuracy; however, they are sensitive to observation noise such as image noise, different lighting conditions, and even occlusion by outliers. The iterative approaches, however, achieve better accuracy than the noniterative methods by solving the rotation matrix with a nonlinear least-square method iteratively. A typical iterative method is the Levenberg-Marquardt (L-M) algorithm [5–7], and it has been widely used and accepted as a standard algorithm for least-square problem in photogrammetry. The L-M method is in essentially the combination of the steepest descent method and the Gauss-Newton method in different optimization stages. The steepest descent method is used at the early stage of optimization when the current value of error is still far from the minimum, while the Gauss-Newton method is used at