DA-IRRK: DATA-ADAPTIVE ITERATIVELY REWEIGHTED ROBUST KERNEL-BASED APPROACH FOR BACK-END OPTIMIZATION IN VISUAL SLAM

DA-IRRK: Data-Adaptive Iteratively Reweighted Robust Kernel-Based Approach for Back-End Optimization in Visual SLAM

Back-end optimization is a key process to eliminate the cumulative error in Visual Simultaneous Localization and Mapping (VSLAM).Existing VSLAM frameworks often use kernel function-based back-end optimization methods.However, these methods typically rely on fixed kernel parameters based on the chi-square test, assuming Gaussian-distributed reprojec

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