MapSegNet: A Fully Automated Model Based on the Encoder-Decoder Architecture for Indoor Map Segmentation-Reference-Cited by-同舟云学术

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MapSegNet: A Fully Automated Model Based on the Encoder-Decoder Architecture for Indoor Map Segmentation

Published:2021 Issue: Volume:9 Page:101530-101542
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Foroughi Farzin,Wang Jikai,Nemati Alireza,Chen Zonghai^ORCID,Pei Haoyuan

Funder

National Natural Science Foundation of China

Chinese Academy of Science-The World Academy of Sciences (CAS-TWAS) President’s Fellowship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9312710/09488192.pdf?arnumber=9488192

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey;Applied Sciences;2024-05-07

2. Automatic Indoor Space Layout Design Based on Deep Reinforcement Learning;2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE);2024-04-26

3. Research on preprocessing algorithm of indoor map partitioning and global path planning based on FAST;2023-11-30

4. S-Graphs+: Real-Time Localization and Mapping Leveraging Hierarchical Representations;IEEE Robotics and Automation Letters;2023-08

5. Research on Map partitioning and Preprocessing Algorithms for Global Path Planning;2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI);2023-05-12

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