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Transformation Decoupling Strategy Based on Screw Theory for Deterministic Point Cloud Registration With Gravity

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    This study introduces a novel method for robust point cloud registration using gravity prior, effectively handling extreme outlier correspondences. The approach enhances computational efficiency and accuracy, even in correspondence-free scenarios.

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    Area of Science:

    • Robotics
    • Computer Vision
    • Geometric Computing

    Background:

    • Point cloud registration is crucial for 3D reconstruction and analysis.
    • Heavy outlier correspondences pose a significant challenge to traditional registration methods.
    • Gravity prior, often from inertial measurement units (IMUs), can simplify registration by reducing rotational degrees of freedom (DOF).

    Purpose of the Study:

    • To develop a robust correspondence-based point cloud registration method using gravity prior.
    • To enhance computational efficiency by decoupling the transformation problem.
    • To achieve reliable registration even with extremely high outlier rates.

    Main Methods:

    • Leveraging screw theory to decouple the 4-DOF registration problem into sequential 1-DOF, 2-DOF, and 1-DOF sub-problems.
    • Employing an interval stabbing method for translation along the rotation axis (1-DOF).
    • Utilizing a branch-and-bound method for the auxiliary pole variable (2-DOF).
    • Implementing a global voting method for rotation angle estimation (1-DOF).

    Main Results:

    • The proposed transformation decoupling strategy significantly enhances computation efficiency.
    • The method demonstrates superior robustness, successfully registering point clouds with outlier rates exceeding 99%.
    • Achieved efficient and deterministic registration through sequential consensus maximization of sub-problems.

    Conclusions:

    • The novel method provides a robust and efficient solution for point cloud registration in the presence of severe outliers.
    • The approach is effective even for correspondence-free registration tasks.
    • Experimental validation confirms the method's superiority over state-of-the-art techniques on synthetic and real-world data.