Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147246
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dc.contributor.authorYue, Yufengen_US
dc.contributor.authorLi, Ruilinen_US
dc.contributor.authorZhao, Chunyangen_US
dc.contributor.authorYang, Chuleen_US
dc.contributor.authorZhang, Junen_US
dc.contributor.authorWen, Mingxingen_US
dc.contributor.authorPeng, Guohaoen_US
dc.contributor.authorWu, Zhenyuen_US
dc.contributor.authorWang, Danweien_US
dc.date.accessioned2021-03-29T04:41:37Z-
dc.date.available2021-03-29T04:41:37Z-
dc.date.issued2019-
dc.identifier.citationYue, Y., Li, R., Zhao, C., Yang, C., Zhang, J., Wen, M., Peng, G., Wu, Z. & Wang, D. (2019). Probabilistic 3D semantic map fusion based on Bayesian rule. 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 542-547. https://dx.doi.org/10.1109/CIS-RAM47153.2019.9095794en_US
dc.identifier.isbn9781728134581-
dc.identifier.urihttps://hdl.handle.net/10356/147246-
dc.description.abstractPerforming collaborative semantic mapping is a critical challenge for multi-robot systems to maintain a comprehensive contextual understanding of the surroundings. In this paper, a novel hierarchical semantic map fusion framework is proposed, where the problem is addressed in low-level single robot semantic mapping and high level global semantic map fusion. In the single robot semantic mapping process, Bayesian rule is used for label fusion and occupancy probability updating, where the semantic information is added to the geometric map grid. High level global semantic map fusion covers decentralized map sharing and global semantic map updating. Collaborative semantic mapping is conducted in two scenarios, that is, NTU dataset and the KITTI dataset. The results show the high quality of the global semantic map, which demonstrates the utility and versatility of 3D semantic map fusion algorithm.en_US
dc.language.isoenen_US
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/https://doi.org/10.1109/CIS-RAM47153.2019.9095794en_US
dc.subjectEngineeringen_US
dc.titleProbabilistic 3D semantic map fusion based on Bayesian ruleen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.conference2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)en_US
dc.identifier.doi10.1109/CIS-RAM47153.2019.9095794-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85085856222-
dc.identifier.spage542en_US
dc.identifier.epage547en_US
dc.subject.keywordsSemanticsen_US
dc.subject.keywordsImage Fusionen_US
dc.citation.conferencelocationBangkok, Thailanden_US
item.grantfulltextopen-
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