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This paper presents a novel non-Gaussian inference algorithm, NormalizingFlow iSAM (NF-iSAM), for solving SLAM problems with non-Gaussian factors and/ornon-linear measurement models. NF-iSAM exploits the expressive power of neuralnetworks, and trains normalizing flows to draw samples from the joint posteriorof non-Gaussian factor graphs. By leveraging the Bayes tree, NF-iSAM is able toexploit the sparsity structure of SLAM, thus enabling efficient incrementalupdates similar to iSAM2, albeit in the more challenging non-Gaussian setting.We demonstrate the performance of NF-iSAM and compare it against thestate-of-the-art algorithms such as iSAM2 (Gaussian) and mm-iSAM (non-Gaussian)in synthetic and real range-only SLAM datasets.

NF-iSAM: Incremental Smoothing and Mapping via Normalizing Flows