The attitude parameter is an important state parameter for a long axisymmetric target. The plane intersection (PI) method is a commonly used method for attitude estimation. However, this method only uses planes’ information in the object space under a multiple camera system (more than two cameras simultaneously observing a target). We propose two methods to address the aforementioned issue. One method involves minimizing the square of the object-space angle residual (OAR) and the other method involves minimizing the square of the image-space angle residual (IAR). The linear optimization method is used for the above minimizing problems. The simulation results demonstrate that the IAR method has higher accuracy than the PI and OAR methods under multiple and dual camera systems because it incorporates information of a pair of corresponding image points. Furthermore, our experiments have shown that the linear method is generally faster, and it has an equivalent accuracy compared to the iterative method.
This paper presents a robust, accurate and real-time model-based tracking method for markerless objects in complex environments to replace the conventional 3D tracking approach based on cooperative targets. A known 3D model of the object is projected onto a 2D plane and occlusion culling is performed with the precalibrated intrinsic parameters and initialized pose. The correspondences between a 3D object model and 2D image edges are commonly used to estimate the camera pose, so the pose optimization problem is transformed into 3D/2D model-to-image registration. For each visible model sample point, a one-dimensional search for putative image edge points is then performed along a direction perpendicular to its line by state-of-the-art methods. However, false correspondences always occur due to cluttered backgrounds or partial occlusion. To overcome this problem, a new search scheme for obtaining line correspondences instead of edge point correspondences is implemented. The outliers of 3D/2D line correspondences are then effectively detected and removed with algebraic outlier rejection, where the camera pose is iteratively optimized from correct correspondences of 3D/2D lines by minimizing the perpendicular distances from the endpoints of 3D model lines to their corresponding projection planes. The method presented in this paper has been validated on both synthetic images and real data. The experimental results show that the method is robust to strong noise, exquisite illumination changes and highly cluttered backgrounds. Meanwhile, it can easily satisfy the real time request.