A polyhedral object recognition algorithm for augmented reality

Registration between cameras and objects is a central element for augmented reality applications and required to combine real and rendered scenes. In this paper, we present a new approach to solve the problem of estimating the camera 3-D location and orientation from a matched set of 3-D model and 2-D image features. An iterative least-square method is used to solve both rotation and translation simultaneously. We derive an error equation using roll-pitch-yaw angle to present the rotation matrix. From the modeling of an error equation, we analytically extract the partial derivates for estimation parameters from the nonlinear error equation. To minimize the error equation, Levenberg-Marquardt algorithm is introduced with uniform sampling strategy of rotation space to avoid stuck in local minimum.