Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual
  Localization
Clémentin Boittiaux Claire Dune Maxime Ferrera Aurélien Arnaubec Ricard Marxer Marjolaine Matabos Loïc Van Audenhaege Vincent Hugel
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/.