# Online Stochastic Variational Gaussian Process Mapping for Large-Scale SLAM in Real Time

arXiv: https://arxiv.org/abs/2211.05601 · HTML: https://mlsanity.com/paper/2211.05601/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization](https://mlsanity.com/paper/1910.06403/) (2019)
- [Towards Autonomous Industrial-Scale Bathymetric Surveying](https://mlsanity.com/paper/2003.12471/) (2020)
- [Data-driven Loop Closure Detection in Bathymetric Point Clouds for Underwater SLAM](https://mlsanity.com/paper/2209.08578/) (2022)
- [Improving the Modularity of AUV Control Systems using Behaviour Trees](https://mlsanity.com/paper/1811.00426/) (2018)

## Abstract (arXiv)

Autonomous underwater vehicles (AUVs) are becoming standard tools for underwater exploration and seabed mapping in both scientific and industrial applications citegraham2022rapid, stenius2022system. Their capacity to dive untethered allows them to reach areas inaccessible to surface vessels and to collect data more closely to the seafloor, regardless of the water depth. However, their navigation autonomy remains bounded by the accuracy of their dead reckoning (DR) estimate of their global position, severely limited in the absence of a priori maps of the area and GPS signal. Global localization systems equivalent to the later exists for the underwater domain, such as LBL or USBL. However they involve expensive external infrastructure and their reliability decreases with the distance to the AUV, making them unsuitable for deep sea surveys.