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/.

翻译：视觉定位在机器人系统对已访问环境进行定位与导航中发挥着重要作用。当跨越长时间周期进行环境重访时，由季节或昼夜循环引发的环境变化构成重大挑战。在水下环境中，变异性源于其他因素，如水质条件变化或海洋生物生长。然而，这一难题仍是研究薄弱环节，部分原因在于缺乏相关数据。本文提出一个新型深海数据集，用于评估水下长期视觉定位技术。该数据集包含五年间对同一热液喷口构造的四次考察影像。通过导航数据与运动恢复结构技术，我们估算出相机位姿及场景共有几何结构，为视觉定位技术评估提供基准参照。数据分析揭示了年际间的主要环境变化特征。此外，多种成熟视觉定位方法在数据集上的评估结果表明，水下长期视觉定位技术仍有显著提升空间。数据集已公开于 https://www.seanoe.org/data/00810/92226/。