General concepts of multi-sensor data-fusion based SLAM
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Date
2020-06-01
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Institute of Advanced Engineering and Science
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Abstract
This paper is approaching a problem of Simultaneous Localization and Mapping (SLAM) algorithms focused specically on processing of data from a heterogeneous set of sensors concurrently. Sensors are considered to be different in a sense of measured physical quantity and so the problem of effective data-fusion is discussed. A special extension of the standard probabilistic approach to SLAM algorithms is presented. This extension is composed of two parts. Firstly is presented general perspective multiple-sensors based SLAM and then thee archetypical special cases are discuses. One archetype provisionally designated as ”partially collective mapping” has been analyzed also in a practical perspective because it implies a promising options for implicit map-level data-fusion.
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International Journal of Robotics and Automation (IJRA). 2020, vol. 9, issue 2, p. 63-72.
http://ijra.iaescore.com/index.php/IJRA/article/view/20258/12906
http://ijra.iaescore.com/index.php/IJRA/article/view/20258/12906
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Peer-reviewed
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en
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Creative Commons Attribution-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-sa/4.0/
http://creativecommons.org/licenses/by-sa/4.0/