Eight solutions of the essential matrix for continuous camera motion tracking in video augmented reality

This paper considers a self-calibration approach to the estimation of motion parameters for an unknown camera used for video-based augmented reality. Whilst existing systems derive four SVD solutions of the essential matrix, which encodes the epipolar geometry between two camera views, this paper presents eight possible solutions derived from mathematical computation and geometrical analysis. The eight solutions not only reflect the position and orientation of the camera in static displacement but also the dynamic, relative orientation between the camera and an object in continuous motion. This paper details a novel algorithm that introduces three geometric constraints to determine the rotation and translation matrix from the eight possible essential matrix solutions. An OpenGL camera motion simulator is used to demonstrate and evaluate the reliability of the proposed algorithms; this directly visualizes the abstract computer vision parameters into real 3D.