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Authors: Christopher Werner ; Sebastian Werner ; René Schöne ; Sebastian Götz and Uwe Aßmann

Affiliation: Software Technology Group, Technische Universität Dresden, Dresden and Germany

Keyword(s): Self-adaptive Systems, Feature Modeling, Robotics, Slam.

Abstract: Mobile autonomous robotic systems need to operate in unknown areas. For this, a plethora of simultaneous localization and mapping (SLAM) approaches has been proposed over the last decades. Although many of these existing approaches have been successfully applied even in real-world productive scenarios, they are typically designed for specific contexts (e.g., in-vs. outdoor, crowded vs. free areas, etc.). Thus, for different contexts, different SLAM algorithms should be used. In this paper, we propose a feature-based classification of SLAM algorithms and a reconfiguration approach to switch between existing SLAM implementations at runtime. By this, mobile robots are enabled to always use the most efficient implementation for their current contexts.

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Paper citation in several formats:
Werner, C., Werner, S., Schöne, R., Götz, S. and Aßmann, U. (2018). Self-adaptive Synchronous Localization and Mapping using Runtime Feature Models. In Proceedings of the 7th International Conference on Data Science, Technology and Applications - EDDY; ISBN 978-989-758-318-6; ISSN 2184-285X, SciTePress, pages 409-418. DOI: 10.5220/0006945504090418

@conference{eddy18,
author={Christopher Werner and Sebastian Werner and René Schöne and Sebastian Götz and Uwe Aßmann},
title={Self-adaptive Synchronous Localization and Mapping using Runtime Feature Models},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - EDDY},
year={2018},
pages={409-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006945504090418},
isbn={978-989-758-318-6},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - EDDY
TI - Self-adaptive Synchronous Localization and Mapping using Runtime Feature Models
SN - 978-989-758-318-6
IS - 2184-285X
AU - Werner, C.
AU - Werner, S.
AU - Schöne, R.
AU - Götz, S.
AU - Aßmann, U.
PY - 2018
SP - 409
EP - 418
DO - 10.5220/0006945504090418
PB - SciTePress

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