用廉价相机分多阶段扫描沉船，实现内外部三维重建
Mapping Pamir: Multi-Session Visual-Inertial SLAM and 3D Reconstruction of an Underwater Shipwreck
- 结合视觉惯性与深度数据，分阶段采集水下影像
- 通过标定靶标对齐不同会话坐标系，完成全局融合
- 首次完整重建加勒比海沉船的外部与可进入内部结构
本文提出一种基于低成本运动相机的水下环境多阶段映射框架。通过潜水电脑记录的水深信息增强视觉-惯性数据，利用开源的SVIn2框架为每阶段生成轨迹与稀疏重建。从SVIn2提取关键帧和相机位姿，采用COLMAP结构光算法进行全局优化，生成目标区域的稠密重建。当存在固定位置的标定靶标时，用于估计各采集会话间的坐标变换，使不同会话数据统一到同一坐标系。该方法应用于巴巴多斯海岸附近一艘沉船的测绘，首次在两个会话中分别完成了沉船外部及可进入内部的建模，并在第三个会话中使用两台视场不同的相机进行补充拍摄。
原文摘要 · Abstract (English)
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.
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