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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/20352


    Title: ROBUST POSE ESTIMATION OF RANDOMLY STACKED RIGID OBJECTS THROUGH VIRTUAL SPACE MODELING AND SIMULATION
    Authors: Wu, Chin-Chia
    Yu-Feng Hsu
    Jwu-Sheng Hu
    Contributor: Industrial Technology Research Institute, Chutung
    Department of Electrical and Control Engineering, National Chiao-Tung University
    Keywords: random bin picking
    pose estimation
    depth sensor
    point cloud
    Date: 2013-11-02
    Issue Date: 2013-11-14 11:41:55 (UTC+8)
    Abstract: Random Bin Picking (RBP) is an important problem in industrial automation,particularly for flexible production line. Since there is no close-form description of the stacking and occlusion situation of parts, the robustness of object pose estimation becomes a challenging issue. Designing a general strategy is even more difficult under the uncertainty of sensor noise and ambient conditions. In this paper, we propose a virtual-space modeling approach to RBP. The virtual space considers the sensor noise and physical models of the environment to render imperfect observations. It enables us to perform individual feature analysis on these observations and obtain prior information associating salient features to view angles. Simulation results show that such a framework can potentially improve the online pose estimation performance for RBP.
    Appears in Collections:[機械工程系所] Automation 2013- The 12th International Conference on Automation Technology

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