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题名:
Robust orientation estimate via inertial guided visual sample consensus
作者: Zhang YL(张吟龙); Liang W(梁炜); Li Y(李杨); An HB(安海博); Tan JD(谈金东)
作者部门: 工业控制网络与系统研究室
通讯作者: Liang W(梁炜) ; Tan JD(谈金东)
关键词: Orientation estimate ; Wearable sensors ; Inertial measurement unit ; Monocular camera ; Sample and consensus
刊名: Personal and Ubiquitous Computing
ISSN号: 1617-4909
出版日期: 2018
卷号: 22, 期号:2, 页码:259–274
收录类别: SCI ; EI
EI收录号: 20173104011957
WOS记录号: WOS:000429358200004
产权排序: 1
项目资助者: National Science Foundation of China under contact 61233007, 61673371 and 71661147005, Youth Innovation Promotion Association, CAS (2015157)
摘要: This paper presents a novel orientation estimate approach named inertial guided visual sample consensus (IGVSAC). This method is intentionally designed for capturing the orientation of human body joints in free-living environments. Unlike the traditional visual-based orientation estimation methods, where outliers among putative image-pair correspondences are removed based on hypothesize-and-verify models such as the computationally costly RANSAC, our approach novelly exploits prior motion information (i.e., rotation and translation) deduced from the quick-response inertial measurement unit (IMU) as the initial body pose to assist camera in removing hidden outliers. In addition, our IGVSAC algorithm is able to ensure estimation accuracy even in the presence of a large quantity of outliers, thanks to its capability of rejecting apparent mismatches. The estimated orientation from the visual sensor is, in turn, able to correct long-term IMU drifts. We conducted extensive experiments to verify the effectiveness and robustness of our IGVSAC algorithm. Comparisons with highly accurate VICON and OptiTrack Motion Tracking Systems prove that our orientation estimate system is quite suitable for capturing human body joints.
语种: 英语
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/20808
Appears in Collections:工业控制网络与系统研究室_期刊论文

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作者单位: 1.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Konxville, TN, 37996, United States

Recommended Citation:
Zhang YL,Liang W,Li Y,et al. Robust orientation estimate via inertial guided visual sample consensus[J]. Personal and Ubiquitous Computing,2018,22(2):259–274.
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