发表机构
University of South Florida; Indian Institute of Technology Madras; Seoul National University（南佛罗里达大学; 印度理工学院马德拉斯分校; 首尔国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SURGE框架，通过因子图融合视觉与声呐观测联合估计ROV轨迹与目标位置，并利用度量位姿进行声呐高斯泼溅，实现更一致定位与紧凑度量重建。
AI 中文摘要
遥控潜水器（ROV）被广泛用于探索和检查水下环境，如洞穴、沉船和水下基础设施。这些任务需要对周围环境进行精确的3D理解，这既依赖于可靠的潜水器定位，也依赖于度量场景重建。然而，在水下，外部定位通常不可用，这要求小型ROV主要依赖机载感知。光学视觉提供丰富的视觉和几何信息，但存在尺度模糊和轨迹漂移的问题，而2D成像声呐提供度量范围但不完整的3D几何。现有的水下重建方法通常分别处理这些限制或假设已知传感器位姿，使得定位和重建脱节。我们提出SURGE，一种相机声呐框架，通过在图因子中整合视觉和声学观测来联合估计ROV轨迹和目标位置，然后使用恢复的度量位姿进行声呐高斯泼溅。在真实水下RGB声呐观测上的实验表明，与传统的基于视觉的位姿估计相比，SURGE显著提高了定位一致性，并产生比RGB高斯泼溅基线更紧凑、天然度量的重建。
英文摘要
Remotely operated vehicles (ROVs) are widely used to explore and inspect underwater environments such as caves, shipwrecks, and submerged infrastructure. These missions require accurate 3D understanding of the surrounding environment, which depends on both reliable vehicle localization and metric scene reconstruction. However, external positioning is often unavailable underwater, requiring small ROVs to rely primarily on onboard perception. Optic vision provides rich visual and geometric information but suffers from scale ambi- guity and trajectory drift, whereas 2D imaging sonar provides metric range but incomplete 3D geometry. Existing underwater reconstruction approaches typically address these limitations separately or assume known sensor poses, leaving localization and reconstruction disconnected. We present SURGE, a camera sonar framework that jointly estimates the ROV trajectory and target location by integrating visual and acoustic observations within a factor graph, then uses the recovered metric poses for sonar Gaussian splatting. Experiments on real underwater RGB sonar observations show that SURGE substantially improves localization consistency over conventional vision based pose estimation and produces a more compact, natively metric reconstruction than RGB Gaussian splatting baselines.
