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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Fast and precise 6D pose estimation of textureless objects using the point cloud and gray image.

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    This study introduces a vision system for accurately estimating the pose of textureless objects using depth and CCD cameras. The system achieves high precision for robotic grasping tasks.

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    Area of Science:

    • Robotics
    • Computer Vision
    • 3D Perception

    Background:

    • Pose estimation for textureless objects is difficult due to limited surface information.
    • Accurate object localization is crucial for robotic manipulation and grasping tasks.

    Purpose of the Study:

    • To develop a fast and precise vision system for measuring the position and orientation of textureless objects.
    • To integrate data from depth and CCD cameras to overcome individual sensor limitations.

    Main Methods:

    • Object segmentation in point clouds using Random Sample Consensus (RANSAC) and local surface normals.
    • Coarse object area segmentation using oriented bounding boxes.
    • Precise pose measurement via view-based matching after transforming point cloud data to image coordinates.

    Main Results:

    • The vision system demonstrates high efficiency and accuracy in pose estimation.
    • Achieved average errors of <2 mm for x/y, <4 mm for z, and 1° for orientation.
    • The system meets the precision requirements for robotic grasping.

    Conclusions:

    • The combined camera approach effectively leverages complementary sensor data.
    • The downscale method and coarse-to-fine strategy enhance processing efficiency.
    • The developed vision system provides a robust solution for textureless object pose estimation in robotics.