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    Kinematic chain based multi-joint capturing using monocular visual-inertial measurements

    Date
    2017-09
    Author
    Zhang, Yinlong
    Liang, Wei
    He, Hongsheng
    Tan, Jindong
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    Citation
    Zhang, Yinlong; Liang, Wei; He, Hongsheng; Tan, Jindong. 2017. Kinematic chain based multi-joint capturing using monocular visual-inertial measurements. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
    Abstract
    Combining light-weight visual and inertial modalities for motion capturing has been popular in robotics researches. There exist scale ambiguity, inaccurate pose estimation with little or no baseline, incremental drifts over time in visual-inertial fusion. Thus, in this paper, we propose a robust motion capturing method based on the multi-joint kinematic chain using monocular visual-inertial sensors. Our method is able to recover monocular visual scale through the joint geometry constraint. Additionally, we take inertial pre-integration to assist visual outlier removal using Maximum A Posteriori method. Ultimately, the kinematic chain model is leveraged to constrain the associated multiple visual-inertial estimation drifts during long time tracking. In the experiments, we conduct multi-joint capturing on a robotic arm. The quality of motion reconstruction is evaluated by comparing the estimated results with the measurements from an optical motion tracking system OptiTrack.
    Description
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    URI
    http://dx.doi.org/10.1109/IROS.2017.8205996
    http://hdl.handle.net/10057/14862
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