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Updated: Jan 14, 2026

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PAVM: Progressive and Adaptive Variance Minimization Algorithm for Robust Registration.

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    A new progressive and adaptive variance minimization (PAVM) algorithm improves point cloud registration by minimizing distortion from structural deviations and abnormal points. This method enhances accuracy for complex component measurement.

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    Area of Science:

    • Computer Vision
    • Geometric Computing
    • Metrology

    Background:

    • Robust rigid point cloud registration is crucial for precise measurement of complex components.
    • Existing algorithms struggle with matching distortions caused by structural deviations, abnormal points, and measurement defects.
    • Current methods like VMM and WPMAVM offer limited improvement, especially with numerous abnormal points.

    Purpose of the Study:

    • To introduce a novel algorithm, Progressive and Adaptive Variance Minimization (PAVM), to overcome limitations in rigid point cloud registration.
    • To enhance robustness against structural deviations and abnormal points in point cloud data.
    • To improve the speed, stability, and universality of point cloud registration for complex components.

    Main Methods:

    • Developed a progressive de-pseudo weight strategy for comprehensive initial optimization.
    • Employed an approximately truncated weight function to reduce the impact of abnormal points.
    • Introduced an adaptive coordination distance function, combining symmetric point-to-plane and point-to-point metrics for improved speed and stability.

    Main Results:

    • The PAVM algorithm demonstrates strong anti-abnormal interference capabilities and quadratic convergence.
    • Experimental results validate significant improvements in convergence stability, matching speed, and universality compared to existing methods.
    • The algorithm effectively handles registration tasks for diverse and complex components.

    Conclusions:

    • The PAVM algorithm offers a robust solution for rigid point cloud registration, effectively addressing distortions and abnormal points.
    • Its enhanced speed, stability, and universality make it suitable for various industrial and scientific applications.
    • PAVM represents a significant advancement in accurate positioning and measurement of complex objects using point cloud data.