Map Management Approach for SLAM in Large-Scale Indoor and Outdoor Areas

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Ehlers, Simon F.G.; Stuede, Marvin; Nuelle, Kathrin; Ortmaier, Tobias: Map Management Approach for SLAM in Large-Scale Indoor and Outdoor Areas. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). Piscataway, NJ : IEEE, 2020, S. 9652-9658. DOI: https://dx.doi.org/10.1109/ICRA40945.2020.9196997

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/10360

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Sum total of downloads: 164




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Abstract: 
This work presents a semantic map management approach for various environments by triggering multiple maps with different simultaneous localization and mapping (SLAM) configurations. A modular map structure allows to add, modify or delete maps without influencing other maps of different areas. The hierarchy level of our algorithm is above the utilized SLAM method. Evaluating laser scan data (e.g. the detection of passing a doorway) triggers a new map, automatically choosing the appropriate SLAM configuration from a manually predefined list. Single independent maps are connected by link-points, which are located in an overlapping zone of both maps, enabling global navigation over several maps. Loop- closures between maps are detected by an appearance-based method, using feature matching and iterative closest point (ICP) registration between point clouds. Based on the arrangement of maps and link-points, a topological graph is extracted for navigation purpose and tracking the global robot's position over several maps. Our approach is evaluated by mapping a university campus with multiple indoor and outdoor areas and abstracting a metrical-topological graph. It is compared to a single map running with different SLAM configurations. Our approach enhances the overall map quality compared to the single map approaches by automatically choosing predefined SLAM configurations for different environmental setups.
License of this version: Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
Document Type: conferenceObject
Publishing status: acceptedVersion
Issue Date: 2020
Appears in Collections:Fakultät für Maschinenbau

distribution of downloads over the selected time period:

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pos. country downloads
total perc.
1 image of flag of Germany Germany 33 20.12%
2 image of flag of China China 33 20.12%
3 image of flag of Vietnam Vietnam 17 10.37%
4 image of flag of United States United States 14 8.54%
5 image of flag of Hong Kong Hong Kong 14 8.54%
6 image of flag of No geo information available No geo information available 11 6.71%
7 image of flag of Italy Italy 11 6.71%
8 image of flag of Turkey Turkey 6 3.66%
9 image of flag of Japan Japan 6 3.66%
10 image of flag of Russian Federation Russian Federation 4 2.44%
    other countries 15 9.15%

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