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    This study introduces uncertainty-guided conformal keypoint detection for satellite pose estimation, providing accurate average poses and reliable uncertainty sets for safety-critical aerospace missions.

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    Area of Science:

    • Aerospace Engineering
    • Computer Vision
    • Robotics

    Background:

    • Satellite pose estimation is vital for aerospace missions, but current methods lack uncertainty quantification for safety-critical operations.
    • Limited on-board computational resources necessitate efficient yet accurate pose estimation techniques.
    • Existing single-point estimations do not meet the rigorous uncertainty requirements of space applications.

    Purpose of the Study:

    • To develop an uncertainty-guided method for satellite pose estimation that provides both accurate average poses and quantifiable pose uncertainty.
    • To address the limitations of existing methods in uncertainty quantification for safety-critical space operations.
    • To improve the reliability and safety of autonomous satellite operations through enhanced pose estimation.

    Main Methods:

    • Proposed uncertainty-guided conformal keypoint detection to generate keypoint inductive conformal prediction (IndCP) sets.
    • Designed an uncertainty propagation strategy to derive pose uncertainty sets from keypoint IndCP sets.
    • Utilized a transformer-based keypoint predictor and Monte Carlo sampling with the perspective-n-point (PnP) algorithm.
    • Constructed pose uncertainty sets using a convex hull of top-n poses based on conformal reprojection error.

    Main Results:

    • The proposed method achieves higher accuracy in average pose estimation compared to existing approaches.
    • Generated pose uncertainty sets reliably cover the true pose with a specified probability.
    • Demonstrated effectiveness on the Spacecraft PosE Estimation challenge Dataset (SPEED) and LineMOD Occlusion (LMO) dataset.

    Conclusions:

    • The developed uncertainty-guided conformal approach enhances satellite pose estimation accuracy and provides crucial uncertainty quantification.
    • This method is suitable for safety-critical aerospace applications requiring reliable pose information.
    • The approach offers a significant advancement in autonomous satellite operations by addressing the accuracy-efficiency-uncertainty tradeoff.