首个融合事件与图像的水下视觉SLAM基准数据集，助力精准水下定位。
AquaticVision: Benchmarking Visual SLAM in Underwater Environment with Events and Frames
- 构建含真实轨迹的水下多模态数据集，支持事件流与图像同步采集。
- 首次提供带地面真值的水下视觉SLAM基准，可量化评估算法性能。
- 适合水下机器人、视觉导航与事件相机研究者参考使用。
许多水下应用（如海上资产巡检）依赖视觉检测与精细三维重建。近年来，水下视觉SLAM系统在海洋机器人研究中备受关注。然而，现有水下视觉SLAM数据集通常缺乏真实轨迹数据，难以仅凭定性结果或COLMAP重建客观比较不同算法性能。本文提出一个新型水下数据集，采用运动捕捉系统获取真实轨迹，并首次公开包含事件流与图像的多模态视觉数据，用于水下视觉定位基准测试。事件相机数据有助于缓解极低光照或浑浊水下环境带来的挑战，推动更鲁棒的水下视觉SLAM算法发展。数据集主页：https://sites.google.com/view/aquaticvision-lias。
原文摘要 · Abstract (English)
Many underwater applications, such as offshore asset inspections, rely on visual inspection and detailed 3D reconstruction. Recent advancements in underwater visual SLAM systems for aquatic environments have garnered significant attention in marine robotics research. However, existing underwater visual SLAM datasets often lack groundtruth trajectory data, making it difficult to objectively compare the performance of different SLAM algorithms based solely on qualitative results or COLMAP reconstruction. In this paper, we present a novel underwater dataset that includes ground truth trajectory data obtained using a motion capture system. Additionally, for the first time, we release visual data that includes both events and frames for benchmarking underwater visual positioning. By providing event camera data, we aim to facilitate the development of more robust and advanced underwater visual SLAM algorithms. The use of event cameras can help mitigate challenges posed by extremely low light or hazy underwater conditions. The webpage of our dataset is https://sites.google.com/view/aquaticvision-lias.
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