Microbathymetry data, in situ observations, and sampling along the 13°20′N and 13°20′N oceanic core complexes (OCCs) reveal mechanisms of detachment fault denudation at the seafloor, links between tectonic extension and mass wasting, and expose the nature of corrugations, ubiquitous at OCCs. In the initial stages of detachment faulting and high‐angle fault, scarps show extensive mass wasting that reduces their slope. Flexural rotation further lowers scarp slope, hinders mass wasting, resulting in morphologically complex chaotic terrain between the breakaway and the denuded corrugated surface. Extension and drag along the fault plane uplifts a wedge of hangingwall material (apron). The detachment surface emerges along a continuous moat that sheds rocks and covers it with unconsolidated rubble, while local slumping emplaces rubble ridges overlying corrugations. The detachment fault zone is a set of anostomosed slip planes, elongated in the along‐extension direction. Slip planes bind fault rock bodies defining the corrugations observed in microbathymetry and sonar. Fault planes with extension‐parallel stria are exposed along corrugation flanks, where the rubble cover is shed. Detachment fault rocks are primarily basalt fault breccia at 13°20′N OCC, and gabbro and peridotite at 13°30′N, demonstrating that brittle strain localization in shallow lithosphere form corrugations, regardless of lithologies in the detachment zone. Finally, faulting and volcanism dismember the 13°30′N OCC, with widespread present and past hydrothermal activity (Semenov fields), while the Irinovskoe hydrothermal field at the 13°20′N core complex suggests a magmatic source within the footwall. These results confirm the ubiquitous relationship between hydrothermal activity and oceanic detachment formation and evolution.

We present an approach for navigating in unknown environments while, simultaneously, gathering information for inspecting underwater structures using an autonomous underwater vehicle (AUV). To accomplish this, we first use our pipeline for mapping and planning collision-free paths online, which endows an AUV with the capability to autonomously acquire optical data in close proximity. With that information, we then propose a reconstruction pipeline to create a photo-realistic textured 3D model of the inspected area. These 3D models are also of particular interest to other fields of study in marine sciences, since they can serve as base maps for environmental monitoring, thus allowing change detection of biological communities and their environment over time. Finally, we evaluate our approach using the Sparus II, a torpedo-shaped AUV, conducting inspection missions in a challenging, real-world and natural scenario.

The paper discusses the main methodologies and key technologies behind the ROBUST project, funded by the European Union within the Horizon 2020 framework, whose goal is the development of an autonomous underwater vehicle manipulator system for deep sea mining exploration. The work focuses on the developed control and perception framework and related mechatronics, and presents the experimental results from the sea trials in Sardinia, Italy, supporting the proposed methodologies.

Underwater range scanning techniques are starting to gain interest in underwater exploration, providing new tools to represent the seafloor. These scans (often) acquired by underwater robots usually result in an unstructured point cloud, but given the common downward-looking or forward-looking configuration of these sensors with respect to the scene, the problem of recovering a piecewise linear approximation representing the scene is normally solved by approximating these 3D points using a heightmap (2.5D). Nevertheless, this representation is not able to correctly represent complex structures, especially those presenting arbitrary concavities normally exhibited in underwater objects. We present a method devoted to full 3D surface reconstruction that does not assume any specific sensor configuration. The method presented is robust to common defects in raw scanned data such as outliers and noise often present in extreme environments such as underwater, both for sonar and optical surveys. Moreover, the proposed method does not need a manual preprocessing step. It is also generic as it does not need any information other than the points themselves to work. This property leads to its wide application to any kind of range scanning technologies and we demonstrate its versatility by using it on synthetic data, controlled laser scans, and multibeam sonar surveys. Finally, and given the unbeatable level of detail that optical methods can provide, we analyze the application of this method on optical datasets related to biology, geology and archeology.

Multi-robot formations are an important advance in recent robotic developments, as they allow a group of robots to merge their capacities and perform surveys in a more convenient way. With the aim of keeping the costs and acoustic communications to a minimum, cooperative navigation of multiple underwater vehicles is usually performed at the control level. In order to maintain the desired formation, individual robots just react to simple control directives extracted from range measurements or ultra-short baseline (USBL) systems. Thus, the robots are unaware of their global positioning, which presents a problem for the further processing of the collected data. The aim of this paper is two-fold. First, we present a global alignment method to correct the dead reckoning trajectories of multiple vehicles to resemble the paths followed during the mission using the acoustic messages passed between vehicles. Second, we focus on the optical mapping application of these types of formations and extend the optimization framework to allow for multi-vehicle geo-referenced optical 3D mapping using monocular cameras. The inclusion of optical constraints is not performed using the common bundle adjustment techniques, but in a form improving the computational efficiency of the resulting optimization problem and presenting a generic process to fuse optical reconstructions with navigation data. We show the performance of the proposed method on real datasets collected within the Morph EU-FP7 project.