Comparison of DSO and ORB-SLAM3 in Low-Light Environments With Auxiliary Lighting and Deep Learning Based Image Enhancing

IF 5.2 2区 计算机科学 Q2 ROBOTICS
Francesco Crocetti, Raffaele Brilli, Alberto Dionigi, Mario L. Fravolini, Gabriele Costante, Paolo Valigi
{"title":"Comparison of DSO and ORB-SLAM3 in Low-Light Environments With Auxiliary Lighting and Deep Learning Based Image Enhancing","authors":"Francesco Crocetti,&nbsp;Raffaele Brilli,&nbsp;Alberto Dionigi,&nbsp;Mario L. Fravolini,&nbsp;Gabriele Costante,&nbsp;Paolo Valigi","doi":"10.1002/rob.22595","DOIUrl":null,"url":null,"abstract":"<p>In the evolving landscape of robotic navigation, the demand for solutions capable of operating in challenging scenarios, such as low-light environments, is increasing. This study investigates the performance of two state-of-the-art (SOTA) visual simultaneous localization and mapping (VSLAM) algorithms, direct sparse odometry (DSO) and ORBSLAM3, in their monocular implementation, in the dark indoor scenarios where the only light source is provided by an auxiliary light system installed on a robot. A modified Pioneer3-DX robot, equipped with a monocular camera, LED bars, and a lux meter, is utilized to collect a novel data set, “LUCID—Lighting Up Campus Indoor Spaces Data Set,” in real-world, low-light indoor environments. The data set includes image sequences enhanced using a generative adversarial network (GAN) to simulate varying levels of image enhancement. Through comprehensive experiments, we assess the performances of the V-SLAM algorithm, considering the critical balance between maintaining adequate auxiliary illumination and enhancing. This study provides insights into the optimization of robotic navigation in lowlight conditions, paving the way for more robust and reliable autonomous navigation systems.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3748-3771"},"PeriodicalIF":5.2000,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22595","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Field Robotics","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rob.22595","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0

Abstract

In the evolving landscape of robotic navigation, the demand for solutions capable of operating in challenging scenarios, such as low-light environments, is increasing. This study investigates the performance of two state-of-the-art (SOTA) visual simultaneous localization and mapping (VSLAM) algorithms, direct sparse odometry (DSO) and ORBSLAM3, in their monocular implementation, in the dark indoor scenarios where the only light source is provided by an auxiliary light system installed on a robot. A modified Pioneer3-DX robot, equipped with a monocular camera, LED bars, and a lux meter, is utilized to collect a novel data set, “LUCID—Lighting Up Campus Indoor Spaces Data Set,” in real-world, low-light indoor environments. The data set includes image sequences enhanced using a generative adversarial network (GAN) to simulate varying levels of image enhancement. Through comprehensive experiments, we assess the performances of the V-SLAM algorithm, considering the critical balance between maintaining adequate auxiliary illumination and enhancing. This study provides insights into the optimization of robotic navigation in lowlight conditions, paving the way for more robust and reliable autonomous navigation systems.

Abstract Image

基于辅助照明和深度学习的低光环境下DSO和ORB-SLAM3图像增强的比较
在不断发展的机器人导航领域,对能够在具有挑战性的场景(如低光环境)下运行的解决方案的需求正在增加。本研究研究了两种最先进的(SOTA)视觉同步定位和测绘(VSLAM)算法,直接稀疏测距(DSO)和ORBSLAM3,在它们的单目实现中,在黑暗的室内场景中,唯一的光源是由安装在机器人上的辅助照明系统提供的。改装后的先锋3- dx机器人配备了单目摄像头、LED条和勒克斯计,用于在现实世界的低光室内环境中收集一套新颖的数据集,即“LUCID-Lighting校园室内空间数据集”。该数据集包括使用生成对抗网络(GAN)增强的图像序列,以模拟不同级别的图像增强。通过综合实验,我们评估了V-SLAM算法的性能,考虑了保持足够的辅助照明和增强照明之间的关键平衡。这项研究为低光照条件下机器人导航的优化提供了见解,为更强大、更可靠的自主导航系统铺平了道路。
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 求助全文
来源期刊
Journal of Field Robotics
Journal of Field Robotics 工程技术-机器人学
CiteScore
15.00
自引率
3.60%
发文量
80
审稿时长
6 months
期刊介绍: The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments. The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.
×
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:604180095
Book学术官方微信