Development of ROS-based Small Unmanned Platform for Acquiring Autonomous Driving Dataset in Various Places and Weather Conditions

Ji-il Park, Minseong Choi, Seungho Han, Yeongseok Lee, J. Cho, Hyoseo Choi, Min-Yyeong Cho, Minyoung Lee, Kyung-Soo Kim
{"title":"Development of ROS-based Small Unmanned Platform for Acquiring Autonomous Driving Dataset in Various Places and Weather Conditions","authors":"Ji-il Park, Minseong Choi, Seungho Han, Yeongseok Lee, J. Cho, Hyoseo Choi, Min-Yyeong Cho, Minyoung Lee, Kyung-Soo Kim","doi":"10.1109/COMPSAC54236.2022.00013","DOIUrl":null,"url":null,"abstract":"As autonomous driving research has actively pro-gressed, software for autonomous vehicles and embedded systems such as Apollo and AutoWare have also been developed, providing a complete set of self-driving modules, including perception, localization and mapping, path planning, prediction, decision making, and control. Most of the researchers currently use these software programs, but many researchers have also studied autonomous driving based on the middleware software termed robot operating system (ROS) before such software was released, especially in academia. Accordingly, we intend to develop ROS-based unmanned RC car equipped with autonomous driving sensors such as LiDAR, radar, VIS/IR cameras, GPS, and IMUs that can provide ROS-based datasets to researchers studying self-driving cars and robots using ROS. In addition, unlike conventional datasets, we acquire dataset not only on road but also pedestrian paths that can be used in both vehicles and robots and provides extreme environmental datasets such as snowfall environments. In this sense, the ROS dataset we created will be helpful to researchers studying autonomous vehicles and robots by using ROS.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"274 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPSAC54236.2022.00013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

As autonomous driving research has actively pro-gressed, software for autonomous vehicles and embedded systems such as Apollo and AutoWare have also been developed, providing a complete set of self-driving modules, including perception, localization and mapping, path planning, prediction, decision making, and control. Most of the researchers currently use these software programs, but many researchers have also studied autonomous driving based on the middleware software termed robot operating system (ROS) before such software was released, especially in academia. Accordingly, we intend to develop ROS-based unmanned RC car equipped with autonomous driving sensors such as LiDAR, radar, VIS/IR cameras, GPS, and IMUs that can provide ROS-based datasets to researchers studying self-driving cars and robots using ROS. In addition, unlike conventional datasets, we acquire dataset not only on road but also pedestrian paths that can be used in both vehicles and robots and provides extreme environmental datasets such as snowfall environments. In this sense, the ROS dataset we created will be helpful to researchers studying autonomous vehicles and robots by using ROS.
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
基于ros的多地点多天气条件下自动驾驶数据采集小型无人平台的研制
随着自动驾驶研究的积极推进,Apollo和AutoWare等自动驾驶汽车和嵌入式系统的软件也被开发出来,提供了一套完整的自动驾驶模块,包括感知、定位和地图绘制、路径规划、预测、决策和控制。目前大多数研究人员都在使用这些软件程序,但在机器人操作系统(ROS)中间件软件发布之前,许多研究人员也在研究基于这种中间件软件的自动驾驶,特别是在学术界。因此,我们计划开发基于ROS的无人驾驶RC汽车,配备激光雷达、雷达、VIS/IR相机、GPS和imu等自动驾驶传感器,可以为使用ROS研究自动驾驶汽车和机器人的研究人员提供基于ROS的数据集。此外,与传统数据集不同,我们不仅获取道路数据集,还获取可用于车辆和机器人的行人路径数据集,并提供极端环境数据集,如降雪环境。从这个意义上说,我们创建的ROS数据集将有助于研究人员使用ROS研究自动驾驶汽车和机器人。
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
自引率
0.00%
发文量
0
期刊最新文献
Category-Aware App Permission Recommendation based on Sparse Linear Model Early Detection of At-Risk Students in a Calculus Course Apple-YOLO: A Novel Mobile Terminal Detector Based on YOLOv5 for Early Apple Leaf Diseases A Safe Route Recommendation Method Based on Driver Characteristics from Telematics Data GSDNet: An Anti-interference Cochlea Segmentation Model Based on GAN
×
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