RobVision - Visually guiding a walking robot through a ship structure

The EU funded ROBVISION project develops a vision system that finds and measures the location of 3D structures with respect to a CAD-model. The main objective is to build an integrated vision system capable of providing adequate information to navigate a walking robot through a ship structure. The key aspect is the integration of a CAD-model to visual measurement and the direct feedback of the measurement results. The objective is to render visual processing robust to deviations in parts and environmental conditions. To achieve this goal a technique is developed that integrates different cues of images to obtain confidence of the measurement result

RobVision - Visually Guiding a Walking Robot through a Ship Structure

The EU funded ROBVISION project develops a vision system that finds and measures the location of 3D structures with respect to a CAD-model. The main objective is to build an integrated vision system capable of providing adequate information to navigate a walking robot through a ship structure. The key aspect is the integration of a CAD-model to visual measurement and the direct feedback of the measurement results. The objective is to render visual processing robust to deviations in parts and environmental conditions. To achieve this goal a technique is developed that integrates different cues of images to obtain confidence of the measurement result. 1 Project Overview Industries using a CAD-system to design parts or working areas need a means of feedback to enable a comparison of designed and manufactured structures. Using vision based on the CAD information is an effective tool to establish this link. For example, the autonomy of a robotic vehicle is needed in several applications in building and inspecting of large structures, such as ship bodies. The navigation of a walking robot will be demonstrated using this vision tool. Furthermore, the vision tool can be used for the task of dimensional measurements of parts

SERONTO: a Socio-Ecological Research and Observation oNTOlogy.

SERONTO is an ontology developed within ALTER-Net, a Long Term Biodiversity, Ecosystem, and Awareness Research Network funded by the European Union. ALTER-Net addresses major biodiversity issues at a European scale. Within this framework SERONTO has been developed to solve the problem of integrating and managing data stored and collected at different locations within the European Union. SERONTO is a product of a group of people with diverse scientific backgrounds. The ontology is a formal description of the concepts and relationships for the most important aspects of biodiversity data derived from monitoring, experiments and investigations. SERONTO is an ontology that enables seamless presentation of data from different origins in a similar conceptual manner. With SERONTO, meta-analysis, data mining, and data presentation should be possible across datasets collected for different purposes. SERONTO consists of a core ontology and a separate unit and dimensions ontology. The core ontology is designed to be the basis for domain specific ontologies (e.g. species, geography, water, vegetation), which extend the concepts and relationships of the core for their specific needs and requirements. The concepts of the core are derived from scientific principles and lean heavily on statistical methodology. Important considerations in designing SERONTO were 1. Repeatability: The ontology should be capable of holding enough meta-data that another person can repeat the experiment or observation at another place and time. It is not obligatory, however, to provide all information for all datasets; for instance, some information may be missing for old datasets. 2. Transparency: It must be possible to record and retrieve meta-data describing what actually happened. SERONTO includes concepts of things going wrong and documenting data collection under less than ideal conditions. If data and meta-data are available in this way, it will be clear what assumptions must be made to combine data and correctly interpret analyses. Important concepts in the SERONTO core are: 1. Investigation item – the research object or experimental unit; 2. Parameters – the measurement, classification and treatment of the investigation item; 3. Value sets – placeholders for time series and other complex data; 4. Reference lists – nominal values, such as species lists; 5. Methods – used for each parameter, including units, scale, and dimensions; 6. Sampling structure – the origin of the research object or population, and the way it was chosen; 7. Groupings of objects, such as experimental blocks, on which observer, time or other aspects are assigned or related to; 8. Additional information, such as actors (observer, observer groups and institutions), project information, etc., can be attached to several different concepts. Each subsequent analysis has to make assumptions. The assumptions of any particular analysis can be found in the deviation between how the data were obtained and the requirements of the analytical method. The presentation will go deeper into the design considerations and the core concepts. Explanations of the concepts, their interrelationships, and their use in subsequent analysis will be given along with examples from different domains