# NF-iSAM: Incremental Smoothing and Mapping via Normalizing Flows

arXiv: https://arxiv.org/abs/2105.05045 · HTML: https://mlsanity.com/paper/2105.05045/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2021.

## Built on by (most cited first, 3 of 3)

- [Discrete-Continuous Smoothing and Mapping](https://mlsanity.com/paper/2204.11936/) (2022)
- [Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows](https://mlsanity.com/paper/2110.00876/) (2021)
- [involve-MI: Informative Planning with High-Dimensional Non-Parametric Beliefs](https://mlsanity.com/paper/2209.11591/) (2022)

## Abstract (arXiv)

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.