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

• Published in: 2017 International Conference on Mechanical, System and Control Engineering (ICMSC) pp. 240 - 244
• Main Authors: Kin Hong Wong, Ying Kin Yu, Ho Yin Fung, Ho Chuen Kam, Kwun Pang Tsui
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2017
• Subjects: Algorithm design and analysis; automatic control; Cameras; Computational modeling; kalman filter; Kalman filters; Pose estimation; Robustness; Solid modeling; virtual reality systems
• DOI: 10.1109/ICMSC.2017.7959479

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.