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