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
Robotic vehicles are already deployed around the globe as a preferred tool for ocean sampling, from surface coastal waters down to the deepest remote locations. They are mainly used to perform routine measurement tasks, with obvious benefits in terms of space and time density, as well as accuracy in the localization of measurements. As the underlying technology matures, new features are being introduced and validated in operational scenarios, enabling new paradigms in ocean observation. This chapter describes the latest developments in marine robotics, presented in different stages of maturity, and sheds light on upcoming features available to the scientific community.

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 emersed (aerial) and immersed (underwater) scenarios must be reconstructed to create a more complete and reliable map that maximizes the observability of all the offshore structures from the wind turbines to the cable arrays, presenting a multi domain scenario.This work proposes the use of an Autonomous Surface Vehicle (ASV) to map both domains simultaneously. As such, it will produce 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. An error of 0.049 m for the odometry estimation is observed with the GPS/IMU fusion for simulated data and 0.07 m for real field tests. The multi-domain map methodology requires an average of 300 ms per iteration to reconstruct the environment, with an error of at most 0.042 m in simulation.

Abstract
To gather hydrological measurements is a difficult task for Autonomous Surface Vessels. It is necessary for precise navigation considering underwater obstacles, shallow and fast water flows, and also mitigate misreadings due to disturbs caused by their propulsion system. To deal with those problems, this paper presents a new topology of an Autonomous Surface Vessel (ASV) based on a catamaran boat with an aerial propulsion system with azimuth control. This set generates an over-actuated 3 Degree of Freedom (DoF) ASV, highly maneuverable and able of operating over the above-mentioned situations. To deal with the high computational cost of the over-actuated control allocation (CA) problem, this paper also proposes a Fast CA (FCA) approach. The FCA breaks the initial nonlinear system into partially-dependent linear subsystems. This approach generates smaller connected systems with overlapping solution spaces, generating fast and robust convergence, especially attractive for embedded control devices. Both proposals, i.e., ASV and FCA, are assessed through mathematical simulations and real scenarios.

Abstract
To perform autonomous tasks, robots in real-world environments must be able to navigate in dynamic and unknown spaces. To do so, they must recognize previously seen places to compensate for accumulated positional deviations. This task requires effective identification of recovered landmarks to produce a consistent map, and the use of binary descriptors is increasing, especially because of their compact representation. The visual Bag-of-Words (BoW) algorithm is one of the most commonly used techniques to perform appearance-based loop closure detection quickly and robustly. Therefore, this paper presents a behavioral evaluation of a conventional BoW scheme based on Oriented FAST and Rotated BRIEF (ORB) features for image similarity detection in challenging scenarios. For each scenario, full-indexing vocabularies are created to model the operating environment and evaluate the performance for recognizing previously seen places similar to online approaches. Experiments were conducted on multiple public datasets containing scene changes, perceptual aliasing conditions, or dynamic elements. The Bag of Binary Words technique shows a good balance to deal with such severe conditions at a low computational cost.

Abstract
This work presents a six degrees of freedom probabilistic scan matching method for registration of 3D underwater sonar scans. Unlike previous works, where local submaps are built to overcome measurement sparsity, our solution develops scan matching directly from the raw sonar data. Our method, based on the probabilistic Iterative Correspondence (pIC), takes measurement uncertainty into consideration while developing the registration procedure. A new probabilistic sensor model was developed to compute the uncertainty of each scan measurement individually. Initial displacement guesses are obtained from a probabilistic dead reckoning approach, also detailed in this document. Experiments, based on real data, demonstrate superior robustness and accuracy of our method with respect to the popular ICP algorithm. An improved trajectory is obtained by integration of scan matching updates in the localization data fusion algorithm, resulting in a substantial reduction of the original dead reckoning drift.