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
In this paper we present a novel method for the acoustic tracking of multiple Autonomous Underwater Vehicles. While the problem of tracking a single moving vehicle has been addressed in the literature, tracking multiple vehicles is a problem that has been overlooked, mostly due to the inherent difficulties on data association with traditional acoustic localization networks. The proposed approach is based on a Probability Hypothesis Density Filter, thus overcoming the data association problem. Our tracker is able not only to successfully estimate the positions of the vehicles, but also their velocities. Moreover, the tracker estimates are labelled, thus providing a way to establish track continuity of the targets. Using real word data, our method is experimentally validated and the performance of the tracker is evaluated. © 2018 Springer Science+Business Media, LLC, part of Springer Nature

Abstract

Abstract
Purpose: This paper aims to present a mosaicking method for underwater robotic applications, whose result can be provided to other perceptual systems for scene understanding such as real-time object recognition. Design/methodology/approach: This method is called robust and large-scale mosaicking (ROLAMOS) and presents an efficient frame-to-frame motion estimation with outlier removal and consistency checking that maps large visual areas in high resolution. The visual mosaic of the sea-floor is created on-the-fly by a robust registration procedure that composes monocular observations and manages the computational resources. Moreover, the registration process of ROLAMOS aligns the observation to the existing mosaic. Findings: A comprehensive set of experiments compares the performance of ROLAMOS to other similar approaches, using both data sets (publicly available) and live data obtained by a ROV operating in real scenes. The results demonstrate that ROLAMOS is adequate for mapping of sea-floor scenarios as it provides accurate information from the seabed, which is of extreme importance for autonomous robots surveying the environment that does not rely on specialized computers. Originality/value: The ROLAMOS is suitable for robotic applications that require an online, robust and effective technique to reconstruct the underwater environment from only visual information. © 2018, Emerald Publishing Limited.

Abstract
Autonomous Surface Vehicles (ASVs) provide the ideal platform to further explore the many opportunities in the cargo shipping industry, by making it more profitable and safer. This paper presents an architecture for the autonomous docking operation, formed by two stages: a maneuver module and, a situational awareness system to detect a mooring facility where an ASV can safely dock. Information retrieved from a 3D LIDAR, IMU and GPS are combined to extract the geometric features of the floating platform and to estimate the relative positioning and orientation of the moor to the ASV. Then, the maneuver module plans a trajectory to a specific position and guarantees that the ASV will not collide with the mooring facility. The approach presented in this paper was validated in distinct environmental and weather conditions such as tidal waves and wind. The results demonstrate the ability of the proposed architecture for detecting the docking platform and safely conduct the navigation towards it, achieving errors up to 0.107 m in position and 6.58 degrees in orientation.