Driven by the increasing number of marine data science applications, there is a growing interest in surveying and exploring the vast, uncharted terrain of the deep sea with robotic platforms. Despite impressive results achieved by many on-land visual mapping algorithms in the past decades, transferring these methods from land to the deep sea remains a challenge due to harsh environmental conditions. Typically, deep-sea exploration involves the use of autonomous underwater vehicles (AUVs) equipped with high-resolution cameras and artificial illumination systems. However, images obtained in this manner often suffer from heterogeneous illumination and quality degradation due to attenuation and scattering, on top of refraction of light rays. All of this together often lets on-land SLAM approaches fail underwater or makes Structure-from-Motion approaches drift or omit difficult images, resulting in gaps, jumps or weakly registered areas. In this work, we present a system that incorporates recent developments in underwater imaging and visual mapping to facilitate automated robotic 3D reconstruction of hectares of seafloor. Our approach is efficient in that it detects and reconsiders difficult, weakly registered areas, to avoid omitting images and to make better use of limited dive time; on the other hand it is computationally efficient; leveraging a hybrid approach combining benefits from SLAM and Structure-from-Motion that runs much faster than incremental reconstructions while achieving at least on-par performance. The proposed system has been extensively tested and evaluated during several research cruises, demonstrating its robustness and practicality in real-world conditions.

翻译：随着海洋数据科学应用的日益增多，利用机器人平台对广阔未知的深海地形进行勘探与探索的需求日益增长。尽管过去几十年中许多陆地视觉地图构建算法取得了显著成果，但由于恶劣的环境条件，将这些方法从陆地移植到深海仍面临挑战。通常，深海探索涉及使用配备高分辨率相机和人工照明系统的自主水下航行器（AUV）。然而，以此方式获取的图像往往存在光照不均、因衰减与散射导致的图像质量下降以及光线折射等问题。这些问题综合起来，常导致陆地SLAM方法在水下失效，或使运动恢复结构（Structure-from-Motion）方法出现漂移、遗漏困难图像，从而产生地图空洞、跳变或弱配准区域。本文提出了一套融合水下成像与视觉地图构建最新进展的系统，旨在实现数公顷海底的自动化机器人三维重建。该方法的高效性体现在：一方面，能够检测并重新处理困难的弱配准区域，避免遗漏图像并更充分利用有限的潜航时间；另一方面，计算效率显著提升，通过融合SLAM与运动恢复结构的混合方法，其运行速度远超增量式重建，同时性能至少持平。该系统已在多次科考航次中得到广泛测试与评估，验证了其在实际条件下的鲁棒性与实用性。