# Overhead Image Factors for Underwater Sonar-based SLAM

arXiv: https://arxiv.org/abs/2202.05811 · HTML: https://mlsanity.com/paper/2202.05811/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2022.

## Built on by (most cited first, 5 of 5)

- [Synthetic Sonar Image Simulation with Various Seabed Conditions for Automatic Target Recognition](https://mlsanity.com/paper/2210.10267/) (2022)
- [RaSCL: Radar to Satellite Crossview Localization](https://mlsanity.com/paper/2504.15899/) (2025)
- [A Sonar-based AUV Positioning System for Underwater Environments with Low Infrastructure Density](https://mlsanity.com/paper/2405.01971/) (2024)
- [Edge-Assisted Multi-Robot Visual-Inertial SLAM with Efficient Communication](https://mlsanity.com/paper/2603.11085/) (2026)
- [Sonar-GPS Fusion for Seabed Mapping in Turbid Shallow Waters with an Autonomous Surface Vehicle](https://mlsanity.com/paper/2605.01949/) (2026)

## Abstract (arXiv)

Simultaneous localization and mapping (SLAM) is a critical capability for any autonomous underwater vehicle (AUV). However, robust, accurate state estimation is still a work in progress when using low-cost sensors. We propose enhancing a typical low-cost sensor package using widely available and often free prior information; overhead imagery. Given an AUV's sonar image and a partially overlapping, globally-referenced overhead image, we propose using a convolutional neural network (CNN) to generate a synthetic overhead image predicting the above-surface appearance of the sonar image contents. We then use this synthetic overhead image to register our observations to the provided global overhead image. Once registered, the transformation is introduced as a factor into a pose SLAM factor graph. We use a state-of-the-art simulation environment to perform validation over a series of benchmark trajectories and quantitatively show the improved accuracy of robot state estimation using the proposed approach. We also show qualitative outcomes from a real AUV field deployment. Video attachment: https://youtu.be/_uWljtp58ks