Underwater monocular SLAM is a challenging problem with applications from autonomous underwater vehicles to marine archaeology. However, existing underwater SLAM methods struggle to produce maps with high-fidelity rendering. In this paper, we propose WaterSplat-SLAM, a novel monocular underwater SLAM system that achieves robust pose estimation and photorealistic dense mapping. Specifically, we couple semantic medium filtering into two-view 3D reconstruction prior to enable underwater-adapted camera tracking and depth estimation. Furthermore, we present a semantic-guided rendering and adaptive map management strategy with an online medium-aware Gaussian map, modeling underwater environment in a photorealistic and compact manner. Experiments on multiple underwater datasets demonstrate that WaterSplat-SLAM achieves robust camera tracking and high-fidelity rendering in underwater environments.

翻译：水下单目SLAM是一个具有挑战性的问题，其应用涵盖从自主水下航行器到海洋考古学等领域。然而，现有的水下SLAM方法难以生成具有高保真渲染效果的地图。本文提出WaterSplat-SLAM，一种新颖的单目水下SLAM系统，能够实现鲁棒的位姿估计与逼真的密集建图。具体而言，我们将语义介质滤波耦合到双视图三维重建前处理中，以实现水下适应的相机跟踪与深度估计。此外，我们提出了一种语义引导的渲染与自适应地图管理策略，结合在线介质感知的高斯地图，以逼真且紧凑的方式建模水下环境。在多个水下数据集上的实验表明，WaterSplat-SLAM能在水下环境中实现鲁棒的相机跟踪与高保真渲染。