Cross-view and Cross-domain Underwater Localization based on Optical Aerial and Acoustic Underwater Images
This work addresses localization for underwater vehicles in partially structured environments like harbors, representing an incremental advancement by extending cross-view matching to cross-domain scenarios.
The study tackled underwater vehicle localization by correlating aerial optical images with underwater acoustic images, achieving improved localization accuracy compared to dead reckoning in a real marina dataset.
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.