发表机构
University of Delaware; Stevens Institute of Technology; Binghamton University; Dartmouth College（特拉华大学; 史蒂文斯理工学院; 宾汉姆顿大学; 达特茅斯学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用经济相机与潜水计算机数据，基于SVIn2和COLMAP框架，提出水下环境多会话映射框架，实现巴巴多斯海岸沉船的多会话映射，首次对沉船内外进行映射，第三个会话采用双相机拓宽视野。
AI 中文摘要
本文提出了一个利用经济实惠的运动相机对水下环境进行多会话映射的框架。视觉惯性数据通过潜水计算机的水深记录进行增强。利用开源视觉惯性同步定位与地图构建（VI-SLAM）框架SVIn2为每个会话生成轨迹和稀疏重建。利用从SVIn2提取的关键帧和估计的相机位姿，采用运动结构（SfM）框架COLMAP进行全局优化并生成目标环境的密集重建。若有固定位置的校准目标，用于估计不同数据采集会话之间的坐标变换，将不同会话转换到同一坐标系。所提流程用于巴巴多斯海岸一艘沉船的映射。首次在两个会话中对沉船的外部和可进入内部进行映射，第三个会话使用了两个不同视野的相机。
英文摘要
This paper presents a framework for multi-session mapping of underwater environments utilizing an affordable action camera. The Visual-Inertial data are augmented by water depth recordings from a dive computer. SVIn2, an open-source VI-SLAM framework, is utilized to generate a trajectory and a sparse reconstruction for each session. Utilizing the keyframes extracted from SVIn2 and the estimated camera poses, a Structure-from-Motion (SfM) framework, COLMAP, is employed for global optimization and to produce a dense reconstruction of the target environment. The presence of calibration targets at fixed locations, when available, is used to estimate the coordinate transformation between different data collection sessions, thus transforming the different sessions into the same coordinate frame. The proposed pipeline is employed for the mapping of a shipwreck off the coast of Barbados. For the first time, both the exterior and the accessible interior parts of the wreck were mapped in two sessions, while a third session employed two cameras with different fields of view.
Comments8 pages, 12 figures. Accepted to ICRA 2026, Vienna, Austria
