# Expectation-Maximization for Adaptive Mixture Models in Graph Optimization

arXiv: https://arxiv.org/abs/1811.04748 · HTML: https://mlsanity.com/paper/1811.04748/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization](https://mlsanity.com/paper/2401.15313/) (2024)
- [Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems](https://mlsanity.com/paper/2505.07043/) (2025)

## Abstract (arXiv)

Non-Gaussian and multimodal distributions are an important part of many recent robust sensor fusion algorithms. In difference to robust cost functions, they are probabilistically founded and have good convergence properties. Since their robustness depends on a close approximation of the real error distribution, their parametrization is crucial. We propose a novel approach that allows to adapt a multi-modal Gaussian mixture model to the error distribution of a sensor fusion problem. By combining expectation-maximization and non-linear least squares optimization, we are able to provide a computationally efficient solution with well-behaved convergence properties. We demonstrate the performance of these algorithms on several real-world GNSS and indoor localization datasets. The proposed adaptive mixture algorithm outperforms state-of-the-art approaches with static parametrization. Source code and datasets are available under https://mytuc.org/libRSF.