Object pose may be determined from a set of 2D image points and corresponding 3D model points, given the camerapsilas intrinsic parameters. In this paper, two new exterior orientation algorithms are proposed and then compared against the Efficient PnP method and orthogonal iteration algorithm. As an alternative to the homogeneous transformation, both algorithms utilize the 3-simplex as a pose parameterization. One algorithm uses a semidefinite program and the other a Gauss-Newton algorithm.

A new vision and inertial pose estimation system was implemented for real-time handheld augmented reality (AR). A sparse set of 3D cone fiducials are utilized for scalable indoor/outdoor tracking, as opposed to traditional planar patterns. The cones are easy to segment and have a large working volume which makes them more suitable for many applications. The pose estimation system receives measurements from the camera and IMU at 30 Hz and 100 Hz respectively. With a dual-core workstation, all measurements can be processed in real-time to update the pose of virtual graphics within the AR display.