Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147246
Title: Probabilistic 3D semantic map fusion based on Bayesian rule
Authors: Yue, Yufeng
Li, Ruilin
Zhao, Chunyang
Yang, Chule
Zhang, Jun
Wen, Mingxing
Peng, Guohao
Wu, Zhenyu
Wang, Danwei
Keywords: Engineering
Issue Date: 2019
Source: Yue, 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-RAM47153.2019.9095794
Abstract: Performing 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.
URI: https://hdl.handle.net/10356/147246
ISBN: 9781728134581
DOI: 10.1109/CIS-RAM47153.2019.9095794
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-RAM47153.2019.9095794
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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