# Variational Filtering with Copula Models for SLAM

arXiv: https://arxiv.org/abs/2008.00504 · HTML: https://mlsanity.com/paper/2008.00504/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2020.

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

- [Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows](https://mlsanity.com/paper/2110.00876/) (2021)

## Abstract (arXiv)

The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots. In most cases the shared dependency between these variables is modeled through a multivariate Gaussian distribution, but there are many situations where that assumption is unrealistic. Our paper shows how it is possible to relax this assumption and perform simultaneous localization and mapping (SLAM) with a larger class of distributions, whose multivariate dependency is represented with a copula model. We integrate the distribution model with copulas into a Sequential Monte Carlo estimator and show how unknown model parameters can be learned through gradient-based optimization. We demonstrate our approach is effective in settings where Gaussian assumptions are clearly violated, such as environments with uncertain data association and nonlinear transition models.