From mooring chain 3D modeling to subsea positioning: Velella project field trials at Floatgen

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Floating offshore wind farms rely on mooring systems to maintain their position and integrity at sea. Throughout their operational life, these critical components require regular inspections to monitor their condition and identify potential degradation.
For operators, one of the challenges is making these inspections repeatable while reducing costs and limiting risks to humans. Being able to follow consistent inspection paths, identify previously inspected areas, and relate inspection data to specific locations on the asset can help make monitoring more effective over time. This is where subsea positioning and vision technology play an essential role.

Discover the latest advancements of the Velella project

As part of the Velella R&D project, we have been contributing to the development of autonomous underwater inspection capabilities, with a particular focus on vision-based close-range positioning.
Following successive development stages involving simulation and controlled testing, discussed in our latest article, our team returned to FLOATGEN, the floating wind turbine installed at the SEM-REV offshore test site operated by Fondation OPEN-C, to evaluate these technologies in real offshore conditions.

Using real-time 3D vision to position the vehicle around subsea assets

Our expertise in subsea photogrammetry provides a foundation for these developments. Using a stereo vision sensor, real-time photogrammetry enables the reconstruction of the surrounding environment in 3D and the estimation of the vehicle’s position relative to the observed structure. In the context of subsea inspections, this information can be used to determine the distance between the vehicle and the object, support close-range navigation, and carry out photogrammetric inspection.

Vision as a positioning sensor also allows the identification of a specific object and initiate a localized, autonomous inspection of that element, enabling the inspection to be repeated across successive campaigns.
This relative positioning capability is particularly valuable when operating close to subsea structures, where maintaining a consistent distance and following the asset are important for inspection. However, vision represents only one part of the positioning challenge.

Combining vision with acoustic and inertial positioning data

During the latest FLOATGEN trials, we worked with data from the vision sensor alongside complementary acoustic and inertial sensors. A dedicated solution was developed as part of the Velella project to visualize these different sources of information during the inspection. As shown in the accompanying video below, the interface provides several complementary views of the operation.
First, the stereo camera imagery supports the inspection itself, while real-time 3D reconstruction alongside the Doppler Velocity Log (DVL) provide spatial information, including the distance from the object. The Inertial Measurement Unit (IMU) data allows to display the vehicle’s relative position and inspection trajectory, helping visualize its movements around the observed asset. Finally, data from the Ultra-Short Baseline (USBL) acoustic positioning system provide a georeferenced position, connecting the local inspection information to a broader spatial reference.

The test interface brings these different data sources together, with vision providing the primary reference for relative positioning and acoustic and inertial sensors offering complementary information. The next step is to fuse these measurements into a single subsea positioning capability, able to adapt which sensors the vehicle relies on most according to the operating conditions and the reliability of the available data. The FLOATGEN trials provide a valuable real-world environment in which to evaluate these developments and further investigate their integration.

Supporting more repeatable and reliable autonomous subsea operations

Within the Velella project, these developments contribute to the wider objective of facilitating autonomous mooring chain inspections. Reliable and robust positioning information can help a subsea vehicle follow a planned inspection trajectory, maintain awareness of its location relative to the asset, and support the documentation of inspected areas, opening the way to fully autonomous subsea inspections.

Looking further ahead, we can envision fleets of unmanned surface vessels (USVs) and autonomous underwater vehicles (AUVs) carrying out offshore wind farm inspections with full operational autonomy, without the need to deploy inspection crews at sea. Over successive campaigns, these capabilities could help operators compare inspection results more consistently and better support maintenance planning.

The potential applications also extend beyond fully autonomous inspections. Positioning information can support autonomous vehicle navigation, but it can also provide valuable assistance to ROV pilots during remotely operated inspections. In both cases, the underlying need remains similar: understanding the vehicle’s position in relation to its environment and providing reliable spatial information throughout the operation.

Building the foundations of IVM's future subsea positioning solution

For us, the work carried out within the Velella project contributes directly to our broader technological roadmap: extending our expertise in subsea vision and 3D reconstruction toward a close-range positioning solution based on sensor fusion.

Our objective is to combine visual information with complementary acoustic and inertial sensors to develop a more robust positioning capability for subsea vehicles. Designed to be integrated into existing robotic platforms, this future solution aims to support both autonomous navigation and operator-assisted subsea operations.

Since 2023, alongside with BW Ideol, Centrale Nantes, OPEN-C Fondation, Ifremer and ENSTA, we’ve been proud participants in the groundbreaking Velella R&D project aimed to prepare the deployment of floating wind farms under optimal economic, social, and environmental conditions. This project was funded by the French State as part of France 2030 operated by ADEME.