NF-iSAM: Incremental Smoothing and Mapping via Normalizing Flows
Qiangqiang Huang, Can Pu, Dehann Fourie, Kasra Khosoussi, Jonathan P. How, John J. Leonard
Abstract
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.
BibTeX
@inproceedings{icra2021_nfisamincrementa,
title = {NF-iSAM: Incremental Smoothing and Mapping via Normalizing Flows},
author = {Qiangqiang Huang and Can Pu and Dehann Fourie and Kasra Khosoussi and Jonathan P. How and John J. Leonard},
booktitle = {ICRA 2021},
year = {2021}
}