# Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows

arXiv: https://arxiv.org/abs/2110.00876 · HTML: https://mlsanity.com/paper/2110.00876/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 74 references that are in the map.
- First posted on arXiv 2 Oct 2021.

## Builds on (most cited first, 22 of 22)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Neural Spline Flows](https://mlsanity.com/paper/1906.04032/) (2019)
- [Input Convex Neural Networks](https://mlsanity.com/paper/1609.07152/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Normalizing Flows on Tori and Spheres](https://mlsanity.com/paper/2002.02428/) (2020)
- [PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking](https://mlsanity.com/paper/1905.09304/) (2019)
- [Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold](https://mlsanity.com/paper/2106.05965/) (2021)
- [Sum-of-Squares Polynomial Flow](https://mlsanity.com/paper/1905.02325/) (2019)
- [Coupling techniques for nonlinear ensemble filtering](https://mlsanity.com/paper/1907.00389/) (2019)
- [On the representation and learning of monotone triangular transport maps](https://mlsanity.com/paper/2009.10303/) (2020)
- [Probabilistic Data Association via Mixture Models for Robust Semantic SLAM](https://mlsanity.com/paper/1909.11213/) (2019)
- [Advances in Inference and Representation for Simultaneous Localization and Mapping](https://mlsanity.com/paper/2103.05041/) (2021)
- [A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM](https://mlsanity.com/paper/2108.01225/) (2021)
- [Advancing Mixture Models for Least Squares Optimization](https://mlsanity.com/paper/2103.02472/) (2021)
- [NF-iSAM: Incremental Smoothing and Mapping via Normalizing Flows](https://mlsanity.com/paper/2105.05045/) (2021)
- [Consensus-Informed Optimization Over Mixtures for Ambiguity-Aware Object SLAM](https://mlsanity.com/paper/2107.09265/) (2021)
- [Nested Sampling for Non-Gaussian Inference in SLAM Factor Graphs](https://mlsanity.com/paper/2109.10871/) (2021)
- [Variational Filtering with Copula Models for SLAM](https://mlsanity.com/paper/2008.00504/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces](https://mlsanity.com/paper/2405.16453/) (2024)

## Abstract (arXiv)

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).