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eSLAM—Self Localisation and Mapping Using Embodied Data

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Abstract

Autonomous mobile robots have the potential to change our everyday life. Unresolved challenges which span a large spectrum of artificial intelligence research need to be answered to progress further towards this vision. This article addresses the problem of robot localisation and mapping, which plays a vital role for robot autonomy in unknown environments. An analysis of the potential for using embodied data is performed, and the notion of direct and indirect embodied data is introduced. Further, the implications of embodied data for an embodied SLAM algorithm are investigated and set into a robotic context.

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Acknowledgements

The work presented here is part of the Project “Intelligent Mobility”, which is funded by the Federal Ministry for Economics and Technology (BMWI) through the German Space Agency (DLR) grant number 50 RA 0907.

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Authors and Affiliations

  1. DFKI—Robotics Innovation Center, Robert-Hooke-Str. 5, 28359, Bremen, Germany

    Jakob Schwendner & Frank Kirchner

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  1. Jakob Schwendner
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  2. Frank Kirchner
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Correspondence to Jakob Schwendner.

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Schwendner, J., Kirchner, F. eSLAM—Self Localisation and Mapping Using Embodied Data. Künstl Intell 24, 241–244 (2010). https://doi.org/10.1007/s13218-010-0033-3

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  • DOI: https://doi.org/10.1007/s13218-010-0033-3

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