ER-Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS IET Cybersystems and Robotics Pub Date : 2024-05-23 DOI:10.1049/csy2.12116
Changjian Jiang, Zeyu Wan, Ruilan Gao, Yu Zhang
{"title":"ER-Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection","authors":"Changjian Jiang,&nbsp;Zeyu Wan,&nbsp;Ruilan Gao,&nbsp;Yu Zhang","doi":"10.1049/csy2.12116","DOIUrl":null,"url":null,"abstract":"<p>The colour-enhanced point cloud map is increasingly being employed in fields such as robotics, 3D reconstruction and virtual reality. The authors propose ER-Mapping (Extrinsic Robust coloured Mapping system using residual evaluation and selection). ER-Mapping consists of two components: the simultaneous localisation and mapping (SLAM) subsystem and the colouring subsystem. The SLAM subsystem reconstructs the geometric structure, where it employs a dynamic threshold-based residual selection in LiDAR-inertial odometry to improve mapping accuracy. On the other hand, the colouring subsystem focuses on recovering texture information from input images and innovatively utilises 3D–2D feature selection and optimisation methods, eliminating the need for strict hardware time synchronisation and highly accurate extrinsic parameters. Experiments were conducted in both indoor and outdoor environments. The results demonstrate that our system can enhance accuracy, reduce computational costs and achieve extrinsic robustness.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12116","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Cybersystems and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/csy2.12116","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

The colour-enhanced point cloud map is increasingly being employed in fields such as robotics, 3D reconstruction and virtual reality. The authors propose ER-Mapping (Extrinsic Robust coloured Mapping system using residual evaluation and selection). ER-Mapping consists of two components: the simultaneous localisation and mapping (SLAM) subsystem and the colouring subsystem. The SLAM subsystem reconstructs the geometric structure, where it employs a dynamic threshold-based residual selection in LiDAR-inertial odometry to improve mapping accuracy. On the other hand, the colouring subsystem focuses on recovering texture information from input images and innovatively utilises 3D–2D feature selection and optimisation methods, eliminating the need for strict hardware time synchronisation and highly accurate extrinsic parameters. Experiments were conducted in both indoor and outdoor environments. The results demonstrate that our system can enhance accuracy, reduce computational costs and achieve extrinsic robustness.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
ER-Mapping:使用残差评估和选择的外在稳健彩色绘图系统
色彩增强点云图在机器人、三维重建和虚拟现实等领域的应用越来越广泛。作者提出了 ER-Mapping(使用残差评估和选择的外在鲁棒彩色绘图系统)。ER-Mapping 由两个部分组成:同步定位与绘图(SLAM)子系统和着色子系统。同步定位与绘图子系统重建几何结构,在激光雷达-惯性里程测量中采用基于阈值的动态残差选择,以提高绘图精度。另一方面,着色子系统侧重于从输入图像中恢复纹理信息,并创新性地利用三维-二维特征选择和优化方法,无需严格的硬件时间同步和高精度的外在参数。实验在室内和室外环境中进行。结果表明,我们的系统可以提高精确度、降低计算成本并实现外在鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
期刊最新文献
Correction-enabled reversible data hiding with pixel repetition for high embedding rate and quality preservation Anti-sloshing control: Flatness-based trajectory planning and tracking control with an integrated extended state observer Internal and external disturbances aware motion planning and control for quadrotors Multi-feature fusion and memory-based mobile robot target tracking system Efficient knowledge distillation for hybrid models: A vision transformer-convolutional neural network to convolutional neural network approach for classifying remote sensing images
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1