This paper addresses real-time dense 3D reconstruction for a resource-constrained Autonomous Underwater Vehicle (AUV). Underwater vision-guided operations are among the most challenging as they combine 3D motion in the presence of external forces, limited visibility, and absence of global positioning. Obstacle avoidance and effective path planning require online dense reconstructions of the environment. Autonomous operation is central to environmental monitoring, marine archaeology, resource utilization, and underwater cave exploration. To address this problem, we propose to use SVIn2, a robust VIO method, together with a real-time 3D reconstruction pipeline. We provide extensive evaluation on four challenging underwater datasets. Our pipeline produces comparable reconstruction with that of COLMAP, the state-of-the-art offline 3D reconstruction method, at high frame rates on a single CPU.

翻译：本文针对资源受限的自主水下航行器（AUV）在实时稠密三维重建中面临的关键挑战展开研究。水下视觉引导作业因融合了三维运动、外部扰动、有限能见度及缺乏全球定位等复杂因素而极具难度。避障与有效路径规划要求实现环境在线稠密重建，自主操作对于环境监测、海洋考古、资源开发及水下洞穴探索至关重要。为此，我们提出基于鲁棒视觉惯性里程计算法SVIn2与实时三维重建管道的解决方案。在四个具有挑战性的水下数据集上的全面评估表明，本方法在单个CPU上以高帧率运行，其重建质量与当前最先进的离线三维重建方法COLMAP相当。