Abstract NEANIAS is a research and innovation action project funded by the European Union under the Horizon 2020 program. The project addresses the challenge of prototyping novel solutions for the underwater, atmospheric and space research communities, creating a collaborative research ecosystem, and contributing to the effective materialization of the European Open Science Cloud (EOSC). NEANIAS drives the co-design, implementation, delivery, and integration into EOSC of innovative thematic and core services, derived from state-of-the-art assets and practices in the target scientific communities. We present the overall NEANIAS ecosystem architecture, with an emphasis on its core visualization services, detailing their specifications and software development plan, and focusing on the underpinning service-oriented architecture for their delivery. We report on the underlying ideas and guiding principles for designing such visualization services, outlining their current release status and future development roadmaps towards Technological Readiness Level (TRL) 8 maturity and EOSC integration.

This paper presents the control and perception framework used in the ROBUST EU Horizon 2020 project, aimed at integrate different technologies for developing an underwater autonomous robotic system for exploring deep-sea mining sites. The vehicle firstly collects data of the initial zone of interest and it selects a sub area which is the most probable to contain a manganese nodule field; then it carries out a low altitude survey. When a possible nodule is detected by the cameras, the vehicle lands on the seafloor, allowing the following fixed based manipulation which is designed to perform the nodule analysis. The work reports the implemented control and perception architecture and the preliminary pool experiments results.

Total body photography is used for early detection of malignant melanoma, primarily as a means of temporal skin surface monitoring. In a prior work, we presented a scanner with a set of algorithms to map and detect changes in pigmented skin lesions, thus demonstrating that it is possible to fully automate the process of total body image acquisition and processing. The key procedure in these algorithms is skin lesion matching that determines whether two images depict the same real lesion. In this paper, we aim to improve it with respect to false positive and negative outcomes. To this end, we developed two novel methods: one based on successive rigid transformations of three-dimensional point clouds and one based on nonrigid coordinate plane deformations in regions of interest around the lesions. In both approaches, we applied a robust outlier rejection procedure based on progressive graph matching. Using the images obtained from the scanner, we created a ground truth dataset tailored to diversify false positive match scenarios. The algorithms were evaluated according to their precision and recall values, and the results demonstrated the superiority of the second approach in all the tests. In the complete interpositional matching experiment, it reached a precision and recall as high as 99.92% and 81.65%, respectively, showing a significant improvement over our original method.

We present a novel method for planning 3D coverage paths for inspection of complex structures on the ocean floor (such as seamounts or coral reefs) using an autonomous underwater vehicle (AUV). Our method initially uses an a priori map to plan a nominal coverage path that allows the AUV to pass its sensors over all points on the target structure. We then go beyond previous approaches in the literature by considering the vehicle's position uncertainty rather than relying on the unrealistic assumption of an idealized path execution. To this aim, we present a replanning algorithm based on stochastic trajectory optimization that reshapes the nominal path to cope with the actual target structure perceived in situ. The replanning algorithm runs onboard the AUV in realtime during the inspection mission, adapting the path according to the measurements provided by the vehicle's range sensing sonars. We demonstrate the efficacy of our method in experiments at sea using the GIRONA 500 AUV where we cover a concrete block of a breakwater structure in a harbor and an underwater boulder rising from 40 m up to 27 m depth. Moreover, we apply state-of-the-art surface reconstruction techniques to the data acquired by the AUV and obtain 3D models of the inspected structures that show the benefits of our planning method for 3D mapping.