Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization

Visual localization plays an important role in positioning and navigation of robotics systems within previously visited environments. When visits occur over long periods of time, changes in the environment related to seasons or day/night cycles present a major challenge. Under water the sources of variability are due to other factors such as water conditions or growth of marine organisms. Yet it remains a major obstacle and a much less studied one partly due to the lack of data. This paper presents a new deep-sea dataset to benchmark underwater long-term visual localization. The dataset is composed of images from four visits to the same hydrothermal vent edifice over the course of five years. Camera poses and a common geometry of the scene were estimated using navigation data and Structure-from-Motion. This serves as a reference when evaluating visual localization techniques.




Eiffel Tower, underwater dataset, visual localization, deep sea


37.17N, 37.17S, -32.16E, -32.16W


Full HD images extracted from ROV Victor6000 HD and 4k cameras.


2015 images, navigation and model
14 GoZIPProcessed data
2016 images, navigation and model
10 GoZIPProcessed data
2018 images, navigation and model
15 GoZIPProcessed data
2020 images, navigation and model
9 GoZIPProcessed data
Global model
256 MoZIPProcessed data
How to cite
Boittiaux Clementin, Dune Claire, Ferrera Maxime, Arnaubec Aurelien, Marxer Ricard, Van Audenhaege Loic, Matabos Marjolaine, Hugel Vincent (2022). Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization. SEANOE. https://doi.org/10.17882/92226

Copy this text