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Title: A hierarchical framework for collaborative probabilistic semantic mapping
Authors: Yue, Yufeng
Zhao, Chunyang
Li, Ruilin
Yang, Chule
Zhang, Jun
Wen, Mingxing
Wang, Yuanzhe
Wang, Danwei
Keywords: Engineering
Issue Date: 2020
Source: Yue, Y., Zhao, C., Li, R., Yang, C., Zhang, J., Wen, M., Wang, Y. & Wang, D. (2020). A hierarchical framework for collaborative probabilistic semantic mapping. 2020 IEEE International Conference on Robotics and Automation (ICRA), 9659-9665.
metadata.dc.contributor.conference: 2020 IEEE International Conference on Robotics and Automation (ICRA)
Abstract: Performing collaborative semantic mapping is a critical challenge for cooperative robots to maintain a comprehensive contextual understanding of the surroundings. Most of the existing work either focus on single robot semantic mapping or collaborative geometry mapping. 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 mathematical modeling of the overall collaborative semantic mapping problem and the derivation of its probability decomposition. In the single robot level, the semantic point cloud is obtained based on heterogeneous sensor fusion model and is used to generate local semantic maps. Since the voxel correspondence is unknown in collaborative robots level, an Expectation-Maximization approach is proposed to estimate the hidden data association, where Bayesian rule is applied to perform semantic and occupancy probability update. The experimental results show the high quality global semantic map, demonstrating the accuracy and utility of 3D semantic map fusion algorithm in real missions.
ISBN: 9781728173955
DOI: 10.1109/ICRA40945.2020.9197261
Schools: School of Electrical and Electronic Engineering 
Rights: © 2020 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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