Recursive Camera Motion Estimation with Trifocal Tensor

We propose an innovative extended Kalman filter (EKF) algorithm for pose tracking using trifocal tensor. In the EKF, a constant velocity motion model is used as the dynamic system and the trifocal tensor constraint is incorporated into the measurement model. The proposed method has the advantages of those structure and motion (SAM) based approaches in that the pose sequence can be computed with no prior information on the scene structure. It also has the strengths of those model based algorithms in which no updating of 3D structure is necessary in the computation. This results in a stable, accurate and an efficient algorithm with low time and space complexity. Experimental results show that our approach outperformed other existing EKFs that tackle the same problem. An extension to the pose tracking algorithm has been made to demonstrate the application of the trifocal constraint to fast recursive 3D structure recovery.