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

TL;DR

This paper introduces a novel cross-domain and cross-view localization framework that correlates aerial optical images with underwater acoustic images to enhance underwater vehicle localization in structured environments.

Contribution

It extends cross-view image matching to cross-domain scenarios, combining aerial optical and underwater acoustic images for improved localization accuracy.

Findings

Improved underwater localization accuracy over dead reckoning.

Validated approach on real marina dataset.

Effective correlation between aerial and underwater images.

Abstract

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.