A low-complexity sensor fusion algorithm based on a fiber-optic gyroscope aided camera pose estimation system  

作　　者：Zhongwei TAN Chuanchuan YANG Yuliang LI Yan YAN Changhong HE Xinyue WANG Ziyu WANG 

机构地区：[1]State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics Engineering and Computer Science, Peking University [2]School of Information Science and Technology, Xiamen University [3]College of Information and Electrical Engineering, China Agricultural University

出　　处：《Science China(Information Sciences)》2016年第4期156-167,共12页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant No. 61275005);International S&T Cooperation Program of China (Grant No. 2015DFG12520);National Natural Science Foundation of China (Grant No. 61571379);State Key Laboratory of Advanced Optical Communication Systems and Networks, China

摘　　要：Visual tracking, as a popular computer vision technique, has a wide range of applications, such as camera pose estimation. Conventional methods for it are mostly based on vision only, which are complex for image processing due to the use of only one sensor. This paper proposes a novel sensor fusion algorithm fusing the data from the camera and the fiber-optic gyroscope. In this system, the camera acquires images and detects the object directly at the beginning of each tracking stage; while the relative motion between the camera and the object measured by the fiber-optic gyroscope can track the object coordinate so that it can improve the effectiveness of visual tracking. Therefore, the sensor fusion algorithm presented based on the tracking system can overcome the drawbacks of the two sensors and take advantage of the sensor fusion to track the object accurately. In addition, the computational complexity of our proposed algorithm is obviously lower compared with the existing approaches（86% reducing for a 0.5 min visual tracking）. Experiment results show that this visual tracking system reduces the tracking error by 6.15% comparing with the conventional vision-only tracking scheme（edge detection）, and our proposed sensor fusion algorithm can achieve a long-term tracking with the help of bias drift suppression calibration.Visual tracking, as a popular computer vision technique, has a wide range of applications, such as camera pose estimation. Conventional methods for it are mostly based on vision only, which are complex for image processing due to the use of only one sensor. This paper proposes a novel sensor fusion algorithm fusing the data from the camera and the fiber-optic gyroscope. In this system, the camera acquires images and detects the object directly at the beginning of each tracking stage; while the relative motion between the camera and the object measured by the fiber-optic gyroscope can track the object coordinate so that it can improve the effectiveness of visual tracking. Therefore, the sensor fusion algorithm presented based on the tracking system can overcome the drawbacks of the two sensors and take advantage of the sensor fusion to track the object accurately. In addition, the computational complexity of our proposed algorithm is obviously lower compared with the existing approaches（86% reducing for a 0.5 min visual tracking）. Experiment results show that this visual tracking system reduces the tracking error by 6.15% comparing with the conventional vision-only tracking scheme（edge detection）, and our proposed sensor fusion algorithm can achieve a long-term tracking with the help of bias drift suppression calibration.

关 键 词：visual tracking camera pose estimation fiber-optic gyroscope low-complexity sensor fusion 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP212[自动化与计算机技术—计算机科学与技术]