SNGR selectively applies nested sampling to refine iSAM2 posteriors in high-condition-number regions of ambiguous SLAM graphs, yielding better local likelihoods at lower cost than exhaustive non-Gaussian methods.
Towards a robust back-end for pose graph SLAM,
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SNGR: Selective Non-Gaussian Refinement for Ambiguous SLAM Factor Graphs
SNGR selectively applies nested sampling to refine iSAM2 posteriors in high-condition-number regions of ambiguous SLAM graphs, yielding better local likelihoods at lower cost than exhaustive non-Gaussian methods.