Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152421
Title: Collaborative semantic understanding and mapping framework for autonomous systems
Authors: Yue, Yufeng
Zhao, Chunyang
Wu, Zhenyu
Yang, Chule
Wang, Yuanzhe
Wang, Danwei
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Yue, Y., Zhao, C., Wu, Z., Yang, C., Wang, Y. & Wang, D. (2020). Collaborative semantic understanding and mapping framework for autonomous systems. IEEE/ASME Transactions On Mechatronics, 26(2), 978-989. https://dx.doi.org/10.1109/TMECH.2020.3015054
Journal: IEEE/ASME Transactions on Mechatronics
Abstract: Performing collaborative semantic mapping is a critical challenge for cooperative robots to enhance their comprehensive contextual understanding of the surroundings. This paper bridges the gap between the advances in collaborative geometry mapping that relies on pure geometry information fusion, and single robot semantic mapping that focuses on integrating continuous raw sensor data. In this paper, a novel hierarchical collaborative probabilistic semantic mapping framework is proposed, where the problem is formulated in a distributed setting. The key novelty of this work is the modelling of the hierarchical semantic map fusion framework and its mathematical derivation of its probability decomposition. At the single robot level, the semantic point cloud is obtained by combining information from heterogeneous sensors and used to generate local semantic maps. At the collaborative robots level, local maps are shared among robots for global semantic map fusion. Since the voxel correspondence is unknown between local maps, an Expectation-Maximization approach is proposed to estimate the hidden data association. Then, Bayesian rule is applied to perform semantic and occupancy probability update. Experimental results on the UAV (Unmanned Aerial Vehicle) and the UGV (Unmanned Ground Vehicle) platforms show the high quality of global semantic maps, demonstrating the accuracy andutility in practical missions.
URI: https://hdl.handle.net/10356/152421
ISSN: 1083-4435
DOI: 10.1109/TMECH.2020.3015054
Rights: © 2020 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/10.1109/TMECH.2020.3015054.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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