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Context in Robotics and Information Fusion

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Context-Enhanced Information Fusion

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Robotics systems need to be robust and adaptable to multiple operational conditions, in order to be deployable in different application domains. Contextual knowledge can be used for achieving greater flexibility and robustness in tackling the main tasks of a robot, namely mission execution, adaptability to environmental conditions, and self-assessment of performance. In this chapter, we review the research work focusing on the acquisition, management, and deployment of contextual information in robotic systems. Our aim is to show that several uses of contextual knowledge (at different representational levels) have been proposed in the literature, regarding many tasks that are typically required for mobile robots. As a result of this survey, we analyze which notions and approaches are applicable to the design and implementation of architectures for information fusion. More specifically, we sketch an architectural framework which enables for an effective engineering of systems that use contextual knowledge, by including the acquisition, representation, and use of contextual information into a framework for information fusion.

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Acknowledgements

This work was supported by ONRG Grant N62909-14-1-N061.

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Authors and Affiliations

  1. Department of Computer, Control, and Management Engineering, Sapienza University of Rome, via Ariosto 25, 00185, Rome, Italy

    Domenico D. Bloisi, Daniele Nardi, Francesco Riccio & Francesco Trapani

Authors
  1. Domenico D. Bloisi
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  2. Daniele Nardi
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  3. Francesco Riccio
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  4. Francesco Trapani
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Corresponding author

Correspondence to Domenico D. Bloisi .

Editor information

Editors and Affiliations

  1. Department Mathematics and Computer Science, University of Udine, Udine, Italy

    Lauro Snidaro

  2. University Carlos III, Department of Computer Science and Engg. University Carlos III, Colmenarejo, Spain

    Jesús García

  3. Center Multisource Info. Fusion & Dept, Univ. at Buffalo, of Industrial and Engg Center Multisource Info. Fusion & Dept, Buffalo, New York, USA

    James Llinas

  4. Information Directorate, Air Force Research Laboratory Information Directorate, Rome, New York, USA

    Erik Blasch

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Bloisi, D.D., Nardi, D., Riccio, F., Trapani, F. (2016). Context in Robotics and Information Fusion. In: Snidaro, L., García, J., Llinas, J., Blasch, E. (eds) Context-Enhanced Information Fusion. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-28971-7_25

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  • DOI: https://doi.org/10.1007/978-3-319-28971-7_25

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