Place recognition using line-junction-lines in urban environments
Place recognition plays a vital role in eliminating accumulated drift from visual odometry in SLAM system. Bag- of-Words (BoW) -based approach is the most popular solution due to its efficiency and robustness. We propose to use Line- Junction-Line (LJL) to build a BoW for place recognition in urban...
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sg-ntu-dr.10356-1418372020-06-11T03:31:52Z Place recognition using line-junction-lines in urban environments Tang, Xiaoyu Fu, Wenhao Jiang, Muyun Peng, Guohao Wu, Zhenyu Yue, Yufeng Wang, Danwei School of Electrical and Electronic Engineering 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM) ST Engineering-NTU Corporate Laboratory Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Place Recognition Structural Information Place recognition plays a vital role in eliminating accumulated drift from visual odometry in SLAM system. Bag- of-Words (BoW) -based approach is the most popular solution due to its efficiency and robustness. We propose to use Line- Junction-Line (LJL) to build a BoW for place recognition in urban environments. LJL is a simple structure of two lines with their intersection. Different from point features which are detected based on pixel intensity patterns, it represents structure with physical existence, which is more robust to challenging scenarios. Moreover, its descriptor is distinctive and encodes the relationship between the two lines. Experiments on KITTI dataset show the effectiveness of the proposed method compared to loop detection using BoW trained with either point or line features. NRF (Natl Research Foundation, S’pore) Accepted version 2020-06-11T03:31:52Z 2020-06-11T03:31:52Z 2020 Conference Paper Tang, X., Fu, W., Jiang, M., Peng, G., Wu, Z., Yue, Y., & Wang, D. (2019). Place recognition using line-junction-lines in urban environments. Proceedings of 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 530-535. doi:10.1109/CIS-RAM47153.2019.9095776 978-1-7281-3459-8 https://hdl.handle.net/10356/141837 10.1109/CIS-RAM47153.2019.9095776 530 535 en SRP5 © 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/10.1109/CIS-RAM47153.2019.9095776 application/pdf |
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Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Place Recognition Structural Information Tang, Xiaoyu Fu, Wenhao Jiang, Muyun Peng, Guohao Wu, Zhenyu Yue, Yufeng Wang, Danwei Place recognition using line-junction-lines in urban environments |
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Place recognition plays a vital role in eliminating accumulated drift from visual odometry in SLAM system. Bag- of-Words (BoW) -based approach is the most popular solution due to its efficiency and robustness. We propose to use Line- Junction-Line (LJL) to build a BoW for place recognition in urban environments. LJL is a simple structure of two lines with their intersection. Different from point features which are detected based on pixel intensity patterns, it represents structure with physical existence, which is more robust to challenging scenarios. Moreover, its descriptor is distinctive and encodes the relationship between the two lines. Experiments on KITTI dataset show the effectiveness of the proposed method compared to loop detection using BoW trained with either point or line features. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Tang, Xiaoyu Fu, Wenhao Jiang, Muyun Peng, Guohao Wu, Zhenyu Yue, Yufeng Wang, Danwei |
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Conference or Workshop Item |
author |
Tang, Xiaoyu Fu, Wenhao Jiang, Muyun Peng, Guohao Wu, Zhenyu Yue, Yufeng Wang, Danwei |
author_sort |
Tang, Xiaoyu |
title |
Place recognition using line-junction-lines in urban environments |
title_short |
Place recognition using line-junction-lines in urban environments |
title_full |
Place recognition using line-junction-lines in urban environments |
title_fullStr |
Place recognition using line-junction-lines in urban environments |
title_full_unstemmed |
Place recognition using line-junction-lines in urban environments |
title_sort |
place recognition using line-junction-lines in urban environments |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/141837 |
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1681059622948962304 |