General concepts of multi-sensor data-fusion based SLAM

Jan Klečka, Karel Horák, Ondřej Boštík

Abstract


This paper is approaching a problem of Simultaneous Localization and Mapping (SLAM) algorithms focused specifically 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.


Keywords


Simultaneous localization and mapping (SLAM); Localization; Mapping; Data fusion; Partially collective mapping

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DOI: http://doi.org/10.11591/ijra.v9i2.pp63-72

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