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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 Go||ZIP||Processed data|
2016 images, navigation and model
|10 Go||ZIP||Processed data|
2018 images, navigation and model
|15 Go||ZIP||Processed data|
2020 images, navigation and model
|9 Go||ZIP||Processed data|
|256 Mo||ZIP||Processed data|