General concepts of multi-sensor data-fusion based SLAM
Alternative metrics PlumXhttp://hdl.handle.net/11012/193374
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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.
KeywordsSimultaneous localization and mapping (SLAM), Localization, Mapping, Data fusion, Partially collective mapping
Document typePeer reviewed
Document versionFinal PDF
SourceINTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION. 2020, vol. 9, issue 2, p. 63-72.