Real-time 3D model-based tracking: combining edge and texture information

This paper proposes a real-time, robust and efficient 3D model-based tracking algorithm. A nonlinear minimization approach is used to register 2D and 3D cues for monocular 3D tracking. The integration of texture information in a more classical nonlinear edge-based pose computation highly increases the reliability of more conventional edge-based 3D tracker. Robustness is enforced by integrating a M-estimator into the minimization process via an iteratively re-weighted least squares implementation. The method presented in this paper has been validated on several video sequences as well as in visual servoing experiments considering various objects. Results show the method to be robust to large motions and textured environments