Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization
This addresses the challenge of underwater visual localization for robotics, which is less studied due to data scarcity, but it is incremental as it primarily provides a new dataset.
The paper tackles the problem of long-term visual localization in deep-sea environments by introducing a new dataset of images from four visits to a hydrothermal vent over five years, showing that current methods still have room for improvement.
Visual localization plays an important role in the 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. An analysis of the data provides insights about the major changes observed throughout the years. Furthermore, several well-established visual localization methods are evaluated on the dataset, showing there is still room for improvement in underwater long-term visual localization. The data is made publicly available at https://www.seanoe.org/data/00810/92226/.