Safety challenges in using AR.Drone to collaborate with humans in indoor environments

Alexandros Lioulemes, Georgios Galatas, V. Metsis, G. Mariottini, F. Makedon
{"title":"Safety challenges in using AR.Drone to collaborate with humans in indoor environments","authors":"Alexandros Lioulemes, Georgios Galatas, V. Metsis, G. Mariottini, F. Makedon","doi":"10.1145/2674396.2674457","DOIUrl":null,"url":null,"abstract":"This paper presents an Unmanned Aerial Vehicle (UAV), based on the AR.Drone platform, which can perform an autonomous navigation in indoor (e.g. corridor, hallway) and industrial environments (e.g. production line). It also has the ability to avoid pedestrians while they are working or walking in the vicinity of the robot. The only sensor in our system is the front camera. For the navigation part our system rely on the vanishing point algorithm, the Hough transform for the wall detection and avoidance, and the HOG descriptors for pedestrian detection using SVM classifier. Our experiments show that our vision navigation procedures are reliable and enable the aerial vehicle to fly without humans intervention and coordinate together in the same workspace. We are able to detect human motion with high confidence of 85% in a corridor and to confirm our algorithm in 80% successful flight experiments.","PeriodicalId":192421,"journal":{"name":"Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2674396.2674457","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19

Abstract

This paper presents an Unmanned Aerial Vehicle (UAV), based on the AR.Drone platform, which can perform an autonomous navigation in indoor (e.g. corridor, hallway) and industrial environments (e.g. production line). It also has the ability to avoid pedestrians while they are working or walking in the vicinity of the robot. The only sensor in our system is the front camera. For the navigation part our system rely on the vanishing point algorithm, the Hough transform for the wall detection and avoidance, and the HOG descriptors for pedestrian detection using SVM classifier. Our experiments show that our vision navigation procedures are reliable and enable the aerial vehicle to fly without humans intervention and coordinate together in the same workspace. We are able to detect human motion with high confidence of 85% in a corridor and to confirm our algorithm in 80% successful flight experiments.
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
在室内环境中使用ar无人机与人类合作的安全挑战
本文提出了一种基于AR.Drone平台的无人机(UAV),它可以在室内(如走廊、走廊)和工业环境(如生产线)中进行自主导航。它还具有避开在机器人附近工作或行走的行人的能力。我们系统里唯一的传感器是前置摄像头。对于导航部分,我们的系统依赖于消失点算法,霍夫变换用于墙壁检测和回避,以及HOG描述符用于使用SVM分类器检测行人。我们的实验表明,我们的视觉导航程序是可靠的,可以使飞行器在没有人工干预的情况下飞行,并在同一工作空间内进行协调。我们能够以85%的高置信度在走廊中检测人体运动,并在80%的成功飞行实验中证实我们的算法。
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
Human movement detection using attitude and heading reference system Context-aware assistive systems at the workplace: analyzing the effects of projection and gamification Investigation of coinciding shipping accident factors with the use of partitional clustering methods Multiple-robot monitoring system based on a service-oriented DBMS A supervised learning approach for fast object recognition from RGB-D data
×
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