Abstract
Offshore wind farms face harsh maritime conditions, prompting the use of sacrificial anodes to prevent rapid structural degradation. Regular maintenance and replacement of these elements are vital to ensure ongoing corrosion protection, maintain structural integrity, and optimize efficiency. This article details the design and validation of the MARESye hybrid underwater imaging system, capable of retrieving heterogeneous tri-dimensional information with millimetric precision for the close-range inspection of submerged critical structures. The optical prowess of the system is first validated during low turbidity trials where the volumetric properties of a decommissioned anode are reconstructed with absolute errors down to 0.0008 m, and its spatial dimensions are depicted with sub-millimeter precision accounting for relative errors as low as 0.31%. MARESye is later equipped as payload in a commercial ROV during areal environment inspection mission at the ATLANTIS Coastal Test Center. This experiment sees the sensor provide live reconstructions of a sacrificial anode, revealing a biofouling layer of approximately 0.0130 m thickness. The assessment of the high-fidelity 2D/3D information obtained from the MARESye sensor demonstrates its potential to enhance the situational awareness of underwater vehicles, fostering reliable O&M procedures.

Abstract
The integration of precise landing capabilities into unmanned aerial vehicles (UAVs) is crucial for enabling autonomous operations, particularly in challenging environments such as the offshore scenarios. This work proposes a heterogeneous perception system that incorporates a multimodal fiducial marker, designed to improve the accuracy and robustness of autonomous landing of UAVs in both daytime and nighttime operations. This work presents ViTAL-TAPE, a visual transformer-based model, that enhance the detection reliability of the landing target and overcomes the changes in the illumination conditions and viewpoint positions, where traditional methods fail. VITAL-TAPE is an end-to-end model that combines multimodal perceptual information, including photometric and radiometric data, to detect landing targets defined by a fiducial marker with 6 degrees-of-freedom. Extensive experiments have proved the ability of VITAL-TAPE to detect fiducial markers with an error of 0.01 m. Moreover, experiments using the RAVEN UAV, designed to endure the challenging weather conditions of offshore scenarios, demonstrated that the autonomous landing technology proposed in this work achieved an accuracy up to 0.1 m. This research also presents the first successful autonomous operation of a UAV in a commercial offshore wind farm with floating foundations installed in the Atlantic Ocean. These experiments showcased the system's accuracy, resilience and robustness, resulting in a precise landing technology that extends mission capabilities of UAVs, enabling autonomous and Beyond Visual Line of Sight offshore operations. © 2025 Wiley Periodicals LLC.

Abstract
The underwater domain presents a myriad of challenges for perception systems that must be overcome to achieve accurate object detection and recognition. To augment the performance and safety of existing solutions for intricate O&M (Operations and Maintenance) procedures, AUVs must perceive the surroundings and locate potential objects of interest based on the perceived information. A depth gradient methodology is employed to survey the seabed using a multibeam sonar to perform a coarse reconstruction of the scenario that it later used to locate and identify foreign objects. This could include rocks, debris, wreckage, or other objects that may pose potential exploratory interest. First results show that the proposed method was able to detect 100 % of the objects present in the scenario with an average chamfer distance error of 0.0238m between models and respective reconstruction. © 2024 IEEE.

Abstract
With the expansion of offshore infrastructure, the necessity for efficient Operation and Maintenance (O&M) procedures intensifies. This article introduces DADDI, a multimodal dataset obtained from a real offshore floating structure, aimed at facilitating comprehensive inspections and 3D model creation. Leveraging Unmanned Aerial Vehicles (UAVs) equipped with advanced sensors, DADDI provides synchronized data, including visual images, thermal images, point clouds, GNSS, IMU, and odometry data. The dataset, gathered during a campaign at the ATLANTIS Coastal Testbed, offers over 2500 samples of each data type, along with intrinsic and extrinsic sensor calibrations. DADDI serves as a vital resource for the development and evaluation of algorithms, models, and technologies tailored to the inspection, monitoring, and maintenance of complex maritime structures. © 2024 IEEE.

Abstract