Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141837
Title: Place recognition using line-junction-lines in urban environments
Authors: Tang, Xiaoyu
Fu, Wenhao
Jiang, Muyun
Peng, Guohao
Wu, Zhenyu
Yue, Yufeng
Wang, Danwei
Keywords: Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
Issue Date: 2020
Source: 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
Project: SRP5
Abstract: 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.
URI: https://hdl.handle.net/10356/141837
ISSN: 978-1-7281-3459-8
DOI: 10.1109/CIS-RAM47153.2019.9095776
Rights: © 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
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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