Abstract
This article presents an innovative hybrid imaging system that provides dense and accurate 3D information from harsh underwater environments. The proposed system is called MARESye and captures the advantages of both active and passive imaging methods: multiple light stripe range (LSR) and a photometric stereo (PS) technique, respectively. This hybrid approach fuses information from these techniques through a data-driven formulation to extend the measurement range and to produce high density 3D estimations in dynamic underwater environments. This hybrid system is driven by a gating timing approach to reduce the impact of several photometric issues related to the underwater environments such as, diffuse reflection, water turbidity and non-uniform illumination. Moreover, MARESye synchronizes and matches the acquisition of images with sub-sea phenomena which leads to clear pictures (with a high signal-to-noise ratio). Results conducted in realistic environments showed that MARESye is able to provide reliable, high density and accurate 3D data. Moreover, the experiments demonstrated that the performance of MARESye is less affected by sub-sea conditions since the SSIM index was 0.655 in high turbidity waters. Conventional imaging techniques obtained 0.328 in similar testing conditions. Therefore, the proposed system represents a valuable contribution for the inspection of maritime structures as well as for the navigation procedures of autonomous underwater vehicles during close range operations.

Abstract
A dense and accurate disparity map is relevant for a large number of applications, ranging from autonomous driving to robotic grasping. Recent developments in machine learning techniques enable us to bypass sensor limitations, such as low resolution, by using deep regression models to complete otherwise sparse representations of the 3D space. This article proposes two main approaches that use a single RGB image and sparse depth information gathered from a variety of sensors/techniques (stereo, LiDAR and Light Stripe Ranging (LSR)): a Convolutional Neural Network (CNN) and a cascade architecture, that aims to improve the results of the first. Ablation studies were conducted to infer the impact of these depth cues on the performance of each model. The models trained with LiDAR sparse information are the most reliable, achieving an average Root Mean Squared Error (RMSE) of 11.8 cm on our own Inhouse dataset; while the LSR proved to be too sparse of an input to compute accurate predictions on its own. © Springer Nature Switzerland AG 2020.

Abstract
The use of robotic solutions in tasks such as the inspection and monitorization of offshore wind farms aims to, not only mitigate the involved risks, but also to reduce the costs of operating and maintaining these structures. Performing a complete inspection of the platforms in useful time is crucial. Therefore, multiple agents can prove to be a cost-effective solution. This work proposes a trajectory planning algorithm, based on the Ant Colony metaheuristic, capable of optimizing the number of Autonomous Surface Vehicles (ASVs) to be used, and their corresponding route. Experiments conducted on a simulated environment, representative of the real scenario, proves this approach to be successful in planning a trajectory that is able to select the appropriate number of agents and the trajectory of each agent that avoids collisions and at the same time guarantees the full observation of the offshore structures.

Abstract
This paper presents a new algorithm for a real-time obstacle avoidance for autonomous surface vehicles (ASV) that is capable of undertaking preemptive actions in complex and challenging scenarios. The algorithm is called adaptive velocity obstacle avoidance (AVOA) and takes into consideration the kinematic and dynamic constraints of autonomous vessels along with a protective zone concept to determine the safe crossing distance to obstacles. A configuration space that includes both the position and velocity of static or dynamic elements within the field-of-view of the ASV is supporting a particle swarm optimization procedure that minimizes the risk of harm and the deviation towards a predefined course while generating a navigation path with capabilities to prevent potential collisions. Extensive experiments demonstrate the ability of AVOA to select a velocity estimative for ASVs that originates a smoother, safer and, at least, two times more effective collision-free path when compared to existing techniques.

Abstract
The offshore wind power industry is an emerging and exponentially growing sector, which calls to a necessity for a cyclical monitoring and inspection to ensure the safety and efficiency of the wind farm facilities. Thus, the multiple domains of the environment must be reconstructed, namely the emersed (aerial) and immersed (underwater) domains, to depict as much as possible the offshore structures from the wind turbines to the cable arrays. This work proposes the use of an Autonomous Surface Vehicle (ASV) to map both environments simultaneously producing a multi-domain map through the fusion of navigational sensors, GPS and IMU, to localize the vehicle and aid the registration process for the perception sensors, 3D Lidar and Multibeam echosounder sonar. The performed experiments demonstrate the ability of the multi-domain mapping architecture to provide an accurate reconstruction of both scenarios into a single representation using the odometry system as the initial seed to further improve the map with data filtering and registration processes.

Abstract
This paper presents a novel dataset, composed of images of objects in two distinct environments and both controlled and uncontrolled capture conditions, aimed at serving as a benchmark for domain-adaptation image classification algorithms in an air versus underwater context. All images are fully annotated, extending the use of the dataset for detection as well as segmentation tasks. An exemplifying use-case is tested, where the performance of a Support Vector Machine applied to a Bag-of-Visual-Words and SIFT features is evaluated on both domains, with different training methodologies. Results demonstrate that the conventional classifier used has a lack of generalization ability, with a poor transfer of knowledge from the aerial to the aquatic domain.