Paper Info
Title
Probabilistic 3D Semantic Map Fusion Based on Bayesian Rule
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.
Year
DOI
Venue
2019
10.1109/CIS-RAM47153.2019.9095794
2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)
Keywords
DocType
ISSN
map sharing,3D semantic map fusion algorithm,Bayesian rule,low-level single robot semantic mapping,high level global semantic map fusion,single robot semantic mapping process,label fusion,occupancy probability,semantic information,geometric map grid,hierarchical semantic map fusion framework,collaborative semantic mapping,probabilistic 3D semantic map fusion algorithm,NTU dataset,KITTI dataset,multirobot systems
Conference
2326-8123
ISBN
Citations 
PageRank 
978-1-7281-3459-8
0
0.34
References 
Authors
9
9
Name
Order
Citations
PageRank
Yufeng Yue185.73
Ruilin Li2167.90
Chunyang Zhao312.03
Chule Yang401.69
Jun Zhang51102188.11
Mingxing Wen625.44
Guohao Peng700.34
Wu Zhenyu813.39
Danwei Wang91529175.13