# Cross-view and Cross-domain Underwater Localization based on Optical Aerial and Acoustic Underwater Images

arXiv: https://arxiv.org/abs/2202.07817 · HTML: https://mlsanity.com/paper/2202.07817/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Wide-Area Image Geolocalization with Aerial Reference Imagery](https://mlsanity.com/paper/1510.03743/) (2015)
- [Cross-View Image Matching for Geo-localization in Urban Environments](https://mlsanity.com/paper/1703.07815/) (2017)
- [Particle Filter Networks with Application to Visual Localization](https://mlsanity.com/paper/1805.08975/) (2018)
- [Semantic Cross-View Matching](https://mlsanity.com/paper/1511.00098/) (2015)

## Built on by (most cited first, 3 of 3)

- [A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection](https://mlsanity.com/paper/2212.00352/) (2022)
- [Scalable Aerial GNSS Localization for Marine Robots](https://mlsanity.com/paper/2505.04095/) (2025)
- [Stable Multi-Drone GNSS Tracking System for Marine Robots](https://mlsanity.com/paper/2511.18694/) (2025)

## Abstract (arXiv)

Cross-view image matches have been widely explored on terrestrial image localization using aerial images from drones or satellites. This study expands the cross-view image match idea and proposes a cross-domain and cross-view localization framework. The method identifies the correlation between color aerial images and underwater acoustic images to improve the localization of underwater vehicles that travel in partially structured environments such as harbors and marinas. The approach is validated on a real dataset acquired by an underwater vehicle in a marina. The results show an improvement in the localization when compared to the dead reckoning of the vehicle.