Advancing Underwater Vision for 3D Phase 2 (AUV3D-P2)

“海底资产的安全高效建设、运营和退役对英国和全球能源生产至关重要。对于海上可再生能源来说，这一点尤其如此，因为成本效率对于提供清洁电力是必要的，这种清洁电力与其他低碳系统相比具有成本竞争力，并且规模可以承受。从建造到退役，水下测量提供数据来监测状况，预测资产寿命并确保环境得到保护。我们的目标是通过从小型远程操作水下航行器提供实时、密集的3D点云数据，实现效率和安全性的飞跃。这将使更小的船只能够使用更少的船员，没有潜水员，并消除了将人们置于危险之中的需要。与传统的视觉测量相比，3D数据可以实现精确的测量和可重复的可靠指标，用于资产状况监测和自主水下航行器（AUV）的自动监测。最终，实时3D技术可为全自主检测AUV提供精确导航，从而进一步减少人力并提高效率。目前，AUV不具备目视检查工作所需的详细绘图和定位。高质量的3D视觉数据也是将人工智能和深度学习解决方案应用于3D图像的先决条件，从而实现更大的自主性和可靠的可重复测量。AUV 3D第2阶段是在成功的第1阶段项目的基础上继续进行的，该阶段项目见证了Roxley开发并展示了从视觉进行实时水下3D重建的技术可行性。这是在海上可再生能源弹射器的Blyth测试设施中进行的，在那里使用带有测试目标的干船坞来测试和评估系统。第二阶段的目标是在基础技术方面进行扩展和改进，并在试验水池和海上进行更具代表性的测试。第一阶段开发的原型可以通过视频进行创新的实时水下3D测量，第二阶段我们将其扩展为更完整的解决方案，考虑到与额外传感器的集成以及将实时测量数据传送到海岸。通过演示在具有挑战性和极端的海底环境中从摄像机生成实时3D数据所需的软件和硬件，能够开发基于完整视觉的水下机器人人工智能（RAI）测量解决方案。这对于创建小型、功能强大、智能的自动驾驶车辆至关重要，并允许更有效的调查，减少人员的伤害。"

"Safe and efficient construction, operation and decommissioning of subsea assets is critically important to UK and worldwide energy production. This is particularly true for offshore renewable energy where cost efficiencies are necessary to deliver clean power that is cost competitive with other low carbon systems and at an affordable scale. From construction to decommissioning, underwater survey provides the data to monitor condition, predict asset life and ensure the environment is protected. We aim to deliver a step change in efficiency and safety by delivering live, dense, 3D point cloud data from small, Remotely Operated Underwater Vehicles. This will enable smaller vessels to be used with fewer crew, no divers, and removing the need to put people at risk. Compared to traditional visual survey, 3D data allows accurate measurement and repeatable, reliable metrics for asset condition monitoring and automatic monitoring from autonomous underwater vehicles (AUVs). Ultimately, live 3D enables accurate navigation for fully autonomous inspection AUVs reducing manpower and increasing efficiency yet further. Currently, AUVs do not possess the detailed mapping and localisation required for visual inspection work. Quality 3D visual data is also a prerequisite to applying artificial intelligence and deep learning solutions to 3D images thereby enabling greater autonomy and reliably repeatable measurements.AUV3D Phase 2 continues from the successful phase-1 project, which saw Rovco develop and demonstrate technical feasibility of live underwater 3D reconstruction from vision. This took place in the Offshore Renewable Energy Catapult's Blyth test facilities, where a dry dock with test targets was used to test and evaluate the system. For phase-2, the goal is to extend and improve on this both in terms of the underpinning technology and with more representative testing both in test tank and at sea.The prototype developed in Phase-1 enables innovative real-time underwater 3D survey from video, and for phase-2 we extend this into a more complete solution, considering integration with additional sensors and the delivery of live survey data to shore.By demonstrating the software and hardware necessary to produce live 3D data from cameras in the challenging and extreme subsea environment we enable the development of a complete vision based underwater Robotic Artificial Intelligence (RAI) survey solution. This is vital to create small, capable, intelligent autonomous vehicles and allow more efficient survey with fewer people in harm's way."