Robust and efficient pose tracking using perspective-four-point algorithm and Kalman filter - Kin Hong Wong; Ying Kin Yu; Ho Yin Fung; Ho Chuen Kam; Kwun Pang Tsui (2017 International Conference on Mechanical, System and Control Engineering (ICMSC), 2017)
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.