Omnidirectional underwater surveying and telepresence

Exploratory dives are traditionally the first step for marine scientists to acquire information on a previously unknown area of scientific interest. Manned submersibles have been the platform of choice for such exploration, as they allow a high level of environmental perception by the scientist on-board, and the ability to take informed decisions on what to explore next. However, manned submersibles have extremely high operation costs and provide very limited bottom time. Remotely operated vehicles (ROVs) can partially address these two issues, but have operational and cost constraints that restrict their usage. This paper discusses new capabilities to assist scientists operating lightweight hybrid remotely operated vehicles (HROV) in exploratory missions of mapping and surveying. The new capabilities, under development within the Spanish National project OMNIUS, provide a new layer of autonomy for HROVs by exploring three key concepts: Omni-directional optical sensing for collaborative immersive exploration, Proximity safety awareness and Online mapping during mission time.Peer Reviewe

Surface reconstruction methods for seafloor modelling

Underwater maps are an important source of information for the scientific community, since mapping the seafloor is the starting point for underwater exploration. The advance of range scanning methodologies (both acoustic and optical), enables the mapping of the seabed to attain increasingly larger resolutions. However, all these techniques sample the surface to reconstruct in the form of a point cloud. Surface reconstruction methods try to recover from these points a continuous surface representing the object in the form of a mesh of triangles, easing visualization and further processing. This thesis proposes four different strategies to tackle the problem of surface reconstruction from point sets corrupted with high levels of noise and outliers, while also recovering the boundaries of these surfaces. The results obtained by our algorithms are discussed and compared both qualitatively and quantitatively with other state-of-the-art approachesEls mapes del fons del marí són una important font d'informació per la comunitat científica. Els avenços en les metodologies d'escaneig (acústic o òptic), permeten que la construcció de mapes del fons marí es realitzi cada cop a més altes resolucions. Tot i això, totes aquestes tècniques mostregen la superfície de l'àrea d’interès en la forma d'un núvol de punts. Els mètodes de reconstrucció de superfícies són els encarregats d’obtenir una superfície continua que representi l'objecte en forma d'una malla de triangles, que facilitarà la visualització i el processat posterior d'aquestes dades. Aquest treball contribueix a l'àrea de reconstrucció de superfícies amb quatre mètodes diferents, capaços de treballar amb núvols de punts que continguin alts nivells de soroll i outliers, i que recuperen al mateix moment els llindars d’aquestes superfícies. Els resultats obtinguts han estat avaluats i comparats tant qualitativament com quantitativament contra altres mètodes de l'estat de l'ar

Splat-based surface reconstruction from defect-laden point sets

We introduce a method for surface reconstruction from point sets that is able to cope with noise and outliers. First, a splat-based representation is computed from the point set. A robust local 3D RANSAC-based procedure is used to filter the point set for outliers, then a local jet surface - a low-degree surface approximation - is fitted to the inliers. Second, we extract the reconstructed surface in the form of a surface triangle mesh through Delaunay refinement. The Delaunay refinement meshing approach requires computing intersections between line segment queries and the surface to be meshed. In the present case, intersection queries are solved from the set of splats through a 1D RANSAC procedur

Autonomous underwater navigation and optical mapping in unknown natural environments

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 scenarioThis work was supported by MORPH, Excellabust, and Roboacademy European projects (FP7-ICT-2011-7-288704, H2020-TWINN-2015 (CSA)-691980, and FP7-PEOPLE-2013-ITN-608096), the ARCHROV Spanish project (DPI2014-57746-C3-3-R), the Generalitat de Catalunya through the ACCIO/TecnioSpring program (TECSPR14-1-0050), and partially supported by the Colombian Government through its Predoctoral Grant Program (No. 568) offered by Colciencia