Robust and efficient pose tracking using perspective-four-point algorithm and Kalman filter

In this paper, we investigate the use of Kalman filter to enable robust tracking based on an efficient pose estimation algorithm, namely the four-point algorithm. Pose estimation is very useful in vision-based system control, for example in automatic driving and virtual reality inputs. Firstly, we have implemented a four-point pose estimation method with a personal computer. This estimation algorithm is supposed to be the method that requires the least number of point features for the generation of a unique solution. On the contrary, existing three-point algorithms may give multiple solutions. Then we have adopted a Kalman filter to enable robust tracking. Kalman filter is computationally efficient and very good at handling noise during tracking. The merge of these two techniques make us able to build a high-speed and yet robust system to be used in a wide variety of real applications. Furthermore, we have shown that a linear Kalman filter can be applied to filter off noises directly from the results of the four-point algorithm. Simulated and real data tests were performed and the results were satisfactory.