一种基于多深度层策略的快速高效线条匹配方法

Qiang Chen, Lingkun Luo, Jiyuan Cai, Shiqiang Hu
{"title":"一种基于多深度层策略的快速高效线条匹配方法","authors":"Qiang Chen, Lingkun Luo, Jiyuan Cai, Shiqiang Hu","doi":"10.1109/ICCSMT54525.2021.00084","DOIUrl":null,"url":null,"abstract":"Lines matching is the significant image pre-processing technique, which plays a central role in 3D reconstruction, visual navigation and other research fields. However, traditional lines matching methods suffered due to issues, e.g., complex processes, low efficiency, and poor matching effect, while those drawbacks strongly hurt the performance as required in the V-SLAM. In this research, we propose a fast and effective lines matching method. Based on the previous research of the fast line detection, we make full use of depth information to construct line features candidate areas to eliminate invalid features and to reduce the computational cost. Then, we use LBD descriptor to inscribe line features, and thereby ensuring the proper lines matching. It is worth noting that, in searching the effectiveness as required by tasks of lines detection and matching, we introduce geometric constraints into our framework. Experiments show that the method proposed in this paper can effectively improve the effectiveness and efficiency of the lines matching in real V-SLAM tasks.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Fast and Efficient Lines Matching Method via Multi-depth-layer Strategy\",\"authors\":\"Qiang Chen, Lingkun Luo, Jiyuan Cai, Shiqiang Hu\",\"doi\":\"10.1109/ICCSMT54525.2021.00084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lines matching is the significant image pre-processing technique, which plays a central role in 3D reconstruction, visual navigation and other research fields. However, traditional lines matching methods suffered due to issues, e.g., complex processes, low efficiency, and poor matching effect, while those drawbacks strongly hurt the performance as required in the V-SLAM. In this research, we propose a fast and effective lines matching method. Based on the previous research of the fast line detection, we make full use of depth information to construct line features candidate areas to eliminate invalid features and to reduce the computational cost. Then, we use LBD descriptor to inscribe line features, and thereby ensuring the proper lines matching. It is worth noting that, in searching the effectiveness as required by tasks of lines detection and matching, we introduce geometric constraints into our framework. Experiments show that the method proposed in this paper can effectively improve the effectiveness and efficiency of the lines matching in real V-SLAM tasks.\",\"PeriodicalId\":304337,\"journal\":{\"name\":\"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSMT54525.2021.00084\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSMT54525.2021.00084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

线条匹配是一种重要的图像预处理技术,在三维重建、视觉导航等研究领域发挥着核心作用。然而,传统的线条匹配方法存在工艺复杂、效率低、匹配效果差等问题,严重影响了V-SLAM的性能要求。在本研究中,我们提出了一种快速有效的线条匹配方法。在前人快速线检测研究的基础上,充分利用深度信息构建线特征候选区域,消除无效特征,降低计算成本。然后,我们使用LBD描述符来刻写线特征,从而保证正确的线匹配。值得注意的是,在搜索线条检测和匹配任务所需的有效性时,我们在框架中引入了几何约束。实验表明,本文提出的方法可以有效提高实际V-SLAM任务中直线匹配的有效性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Fast and Efficient Lines Matching Method via Multi-depth-layer Strategy
Lines matching is the significant image pre-processing technique, which plays a central role in 3D reconstruction, visual navigation and other research fields. However, traditional lines matching methods suffered due to issues, e.g., complex processes, low efficiency, and poor matching effect, while those drawbacks strongly hurt the performance as required in the V-SLAM. In this research, we propose a fast and effective lines matching method. Based on the previous research of the fast line detection, we make full use of depth information to construct line features candidate areas to eliminate invalid features and to reduce the computational cost. Then, we use LBD descriptor to inscribe line features, and thereby ensuring the proper lines matching. It is worth noting that, in searching the effectiveness as required by tasks of lines detection and matching, we introduce geometric constraints into our framework. Experiments show that the method proposed in this paper can effectively improve the effectiveness and efficiency of the lines matching in real V-SLAM tasks.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信