  This paper presents normalizing flows for incremental smoothing and mapping
(NF-iSAM), a novel algorithm for inferring the full posterior distribution in
SLAM problems with nonlinear measurement models and non-Gaussian factors.
NF-iSAM exploits the expressive power of neural networks, and trains
normalizing flows to model and sample the full posterior. By leveraging the
Bayes tree, NF-iSAM enables efficient incremental updates similar to iSAM2,
albeit in the more challenging non-Gaussian setting. We demonstrate the
advantages of NF-iSAM over state-of-the-art point and distribution estimation
algorithms using range-only SLAM problems with data association ambiguity.
NF-iSAM presents superior accuracy in describing the posterior beliefs of
continuous variables (e.g., position) and discrete variables (e.g., data
association).
