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CoLi-BA: Compact Linearization based Solver for Bundle Adjustment.

Zhichao Ye, Guanglin Li, Haomin Liu

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    Summary
    This summary is machine-generated.

    This study introduces CoLi-BA, a faster bundle adjustment (BA) solver for Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SfM). It significantly speeds up optimization for Augmented Reality applications without losing accuracy.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Augmented Reality

    Background:

    • Bundle Adjustment (BA) is crucial for real-time SLAM and large-scale SfM in Augmented Reality.
    • Existing BA solvers face efficiency challenges, limiting real-time performance and scalability.

    Purpose of the Study:

    • To develop a novel and efficient BA solver, named CoLi-BA.
    • To significantly improve optimization speed for BA in SLAM and SfM.

    Main Methods:

    • Introduced compact linearization, replacing redundant Jacobian matrix representations with 3D vectors.
    • Implemented a novel reordering strategy to enhance Schur complement cache efficiency.
    • Reduced computational complexity, memory storage, and cache misses during Hessian matrix construction.

    Main Results:

    • CoLi-BA achieves five times the speed of Ceres and two times the speed of g2o, maintaining accuracy.
    • Local BA in SLAM takes approximately 20ms per frame on a desktop PC.
    • Reconstruction of 7,000 photos in SfM completed in under 30 minutes.

    Conclusions:

    • CoLi-BA offers a substantial speedup for bundle adjustment in SLAM and SfM.
    • The solver's efficiency and accuracy make it suitable for real-time AR applications and large-scale reconstructions.
    • CoLi-BA demonstrates practical effectiveness when integrated into existing open-source SLAM and SfM systems.