Ecosystem Monitoring and Port Surveillance Systems

International audienceIn this project, we should build up a novel system able to perform a sustainable and long term monitoring coastal marine ecosystems and enhance port surveillance capability. The outcomes will be based on the analysis, classification and the fusion of a variety of heterogeneous data collected using different sensors (hydrophones, sonars, various camera types, etc). This manuscript introduces the identified approaches and the system structure. In addition, it focuses on developed techniques and concepts to deal with several problems related to our project. The new system will address the shortcomings of traditional approaches based on measuring environmental parameters which are expensive and fail to provide adequate large-scale monitoring. More efficient monitoring will also enable improved analysis of climate change, and provide knowledge informing the civil authority's economic relationship with its coastal marine ecosystems

Localization, Mapping and SLAM in Marine and Underwater Environments

The use of robots in marine and underwater applications is growing rapidly. These applications share the common requirement of modeling the environment and estimating the robots’ pose. Although there are several mapping, SLAM, target detection and localization methods, marine and underwater environments have several challenging characteristics, such as poor visibility, water currents, communication issues, sonar inaccuracies or unstructured environments, that have to be considered. The purpose of this Special Issue is to present the current research trends in the topics of underwater localization, mapping, SLAM, and target detection and localization. To this end, we have collected seven articles from leading researchers in the field, and present the different approaches and methods currently being investigated to improve the performance of underwater robots

P-8 and the Trilateral Partnership. The operational significance and influence on Norwegian security policy

The background for and the strategic context of this thesis is the threat posed by Russian submarines to Norway and NATO in the North Atlantic. In light of this, the study examines the significance of the P-8 and the trilateral partnership of Norway, the United Kingdom and the United States, by asking two research questions: - What is the operational significance of the P-8 and the trilateral partnership? - How does the P-8 and the trilateral partnership influence Norwegian security policy? It is an explorative and inductive study, which answers the research questions through a qualitative analysis. The thesis uses deterrence, crisis stability and maritime airpower theory, as well as Norwegian Security policy and defence concept, with emphasis on integration and reassurance to frame the discussion. The thesis concludes that the P-8 will provide Norwegian decision-makers with an agile platform with significantly improved capability for ASW and ISR. The trilateral partnership integrates the three partner nations and improves NATOs ASW capability when facing Russian submarines in the North Atlantic. The P-8 and the partnership influence deterrence positively by integrating Norway, NATO and the US, and provide tools for improving crisis stability. However, given the impermanency of American Poseidons, there is a need to establish a trilateral P-8 concept that merges training, exorcises and operations as a signal of presence and integration in the North Atlantic. The contribution of the P-8 in Norway’s policy of reassuring Russia is important as Norway increasingly develops a force structure with so-called offensive strategic capabilities. Of particular importance to reassurance is enhancements in intelligence contributions. Provided the strategic context, improved capabilities and the Norwegian force structure, the influence of the P-8 is decisive for the Norwegian security policy.Bakgrunnen for denne oppgaven og den strategiske konteksten er trusselen Russiske ubåter utgjør for Norge og NATO i Nord-Atlanteren. I lys av dette undersøker oppgaven betydningen av P-8 og trilateralt samarbeidet mellom Norge, Storbritannia og USA, gjennom å stille to forskningsspørsmål: - Hva er den operative betydningen av P-8 og det trilaterale partnerskapet? - Hvordan påvirker P-8 og det trilaterale partnerskapet norsk sikkerhetspolitikk? Studien er eksplorativ og induktiv, og benytter kvalitativ metode for å besvare forskningsspørsmålene. Analysen nyttiggjør teorier for avskrekking, krisestabilitet og maritim luftmakt, i tillegg til norsk sikkerhetspolitikk med vekt på integrasjon og beroligelse, som rammeverk for diskusjonen. Oppgaven konkluderer med at P-8 vil tilføre norske beslutningstakere en fleksibel plattform som i betydelig grad bedrer evnen til anti-ubåt krigføring (ASW) og informasjonsinnhenting (ISR). Det trilaterale samarbeidet integrerer de tre partnernasjonene og bedrer NATOs evne til ASW i møte med russiske ubåter. P-8 og det trilaterale partnerskapet påvirker avskrekking positivt gjennom integrering av Norge, NATO og USA, og tilbyr muligheter for å bedre krisestabiliteten. På en annen side er det forventet at amerikansk tilstedeværelse med P-8 vil være av en mindre permanent karakter. Dette medfører et behov for et trilateralt P-8 konsept som sammenslår trening, øvelser og operasjoner i Nord-Atlanteren. P-8s bidrag innen beroligelse er av sentral karakter, ettersom Norge i økende grad satser på avskrekkende kapabiliteter i styrkestrukturen. Spesielt viktig er økt kapasitet innen etterretningsbidragene. Med utgangspunkt i den strategiske konteksten, forbedrede kapabiliteter og den norske styrkestrukturen, er innflytelsen til maritime patruljefly av avgjørende betydning for norsk sikkerhetspolitik

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Multimodal learning from visual and remotely sensed data

Autonomous vehicles are often deployed to perform exploration and monitoring missions in unseen environments. In such applications, there is often a compromise between the information richness and the acquisition cost of different sensor modalities. Visual data is usually very information-rich, but requires in-situ acquisition with the robot. In contrast, remotely sensed data has a larger range and footprint, and may be available prior to a mission. In order to effectively and efficiently explore and monitor the environment, it is critical to make use of all of the sensory information available to the robot. One important application is the use of an Autonomous Underwater Vehicle (AUV) to survey the ocean floor. AUVs can take high resolution in-situ photographs of the sea floor, which can be used to classify different regions into various habitat classes that summarise the observed physical and biological properties. This is known as benthic habitat mapping. However, since AUVs can only image a tiny fraction of the ocean floor, habitat mapping is usually performed with remotely sensed bathymetry (ocean depth) data, obtained from shipborne multibeam sonar. With the recent surge in unsupervised feature learning and deep learning techniques, a number of previous techniques have investigated the concept of multimodal learning: capturing the relationship between different sensor modalities in order to perform classification and other inference tasks. This thesis proposes related techniques for visual and remotely sensed data, applied to the task of autonomous exploration and monitoring with an AUV. Doing so enables more accurate classification of the benthic environment, and also assists autonomous survey planning. The first contribution of this thesis is to apply unsupervised feature learning techniques to marine data. The proposed techniques are used to extract features from image and bathymetric data separately, and the performance is compared to that with more traditionally used features for each sensor modality. The second contribution is the development of a multimodal learning architecture that captures the relationship between the two modalities. The model is robust to missing modalities, which means it can extract better features for large-scale benthic habitat mapping, where only bathymetry is available. The model is used to perform classification with various combinations of modalities, demonstrating that multimodal learning provides a large performance improvement over the baseline case. The third contribution is an extension of the standard learning architecture using a gated feature learning model, which enables the model to better capture the ‘one-to-many’ relationship between visual and bathymetric data. This opens up further inference capabilities, with the ability to predict visual features from bathymetric data, which allows image-based queries. Such queries are useful for AUV survey planning, especially when supervised labels are unavailable. The final contribution is the novel derivation of a number of information-theoretic measures to aid survey planning. The proposed measures predict the utility of unobserved areas, in terms of the amount of expected additional visual information. As such, they are able to produce utility maps over a large region that can be used by the AUV to determine the most informative locations from a set of candidate missions. The models proposed in this thesis are validated through extensive experiments on real marine data. Furthermore, the introduced techniques have applications in various other areas within robotics. As such, this thesis concludes with a discussion on the broader implications of these contributions, and the future research directions that arise as a result of this work