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dc.contributor.authorAzizzul, Zen_NZ
dc.contributor.authorYeap, Wen_NZ
dc.contributor.editorAirenti, Gen_NZ
dc.contributor.editorBara, BGen_NZ
dc.contributor.editorSandini, Gen_NZ
dc.date.accessioned2018-01-24T23:37:45Z
dc.date.available2018-01-24T23:37:45Z
dc.date.copyright2015en_NZ
dc.identifier.citationIn: Proceedings of the EuroAsianPacific Joint Conference on Cognitive Science, 4th European Conference on Cognitive Science, 11th International Conference on Cognitive Science, Torino, Italy, September 25-27, 2015, pp. 459-464.
dc.identifier.urihttp://hdl.handle.net/10292/11138
dc.description.abstractThis paper shows how an indoor mobile robot equipped with a laser sensor and an odometer computes its global map by associating landmarks found in the environment. The approach developed is based on the observation that humans and animals detects where they are in the surrounding by comparing their spatial relation to some known or recognized objects in the environments, i.e. landmarks. In this case, landmarks are defined as 2D surfaces detected in the robot’s surroundings. They are recognised if they are detected in two successive views. From a cognitive standpoint, this work is inspired by two assumptions about the world; (a) the world is relatively stable and (2) there is a significant overlap of spatial information between successive views. In the implementation, the global map is first initialised with the robot’s first view, and then updated each time landmarks are found at every two successive views. The difference here is, where most robot mapping work integrates everything they see in their update, this work takes advantage of updating only the landmarks before adding the nearby objects associated with them. By association, the map is built without error corrections and the final map produced is not metrically precise.
dc.publisherCEUR-WS.org
dc.relation.urihttp://ceur-ws.org/Vol-1419/paper0074.pdf
dc.rightsCopyright © 2015 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.
dc.subjectInexact map; Landmark association; Autonomous robot
dc.titleAutonomous robot mapping by landmark associationen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
pubs.elements-id320719
aut.relation.conferenceEAP Joint Conference on Cognitive Scienceen_NZ


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