Safe Avoidance Region Detection for Unmanned Aerial Vehicle Using Cues from Expansion of Feature Points

Muhammad Faiz Bin Ramli, A. Sutjipto, Erwin Sulaeman, Ari Legowo
{"title":"Safe Avoidance Region Detection for Unmanned Aerial Vehicle Using Cues from Expansion of Feature Points","authors":"Muhammad Faiz Bin Ramli, A. Sutjipto, Erwin Sulaeman, Ari Legowo","doi":"10.4028/p-zfls0d","DOIUrl":null,"url":null,"abstract":"Develop an obstacle detection system for Unmanned Aerial Vehicle (UAV) especially for small UAV is challenging. A robust system should be able to not only detect obstacles but the free region for the avoidance path as well. Besides, the configuration of the obstacles in the operating environment should never be disregard. In this paper, expansion cues from the detected feature points with the help of convex hull will be used to categorize the regions in the image frame. A micro LIDAR sensor is used as the initial detector of obstacle and queue for image capturing by the camera. Next, ORB algorithm is applied to find the obstacle regions and free space regions. This is done through the principal of object size changes and distance relationship in an image perspective. The proposed system was evaluated through series of experiments in a real environment which consist of different configuration of obstacles. The experiments show the proposed system was able to find the safe avoidance region regardless of the configuration of the obstacles in the operating environment. Keywords: Expansion cue; ORB; Feature points; Safe avoidance region","PeriodicalId":512976,"journal":{"name":"Engineering Headway","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Headway","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4028/p-zfls0d","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Develop an obstacle detection system for Unmanned Aerial Vehicle (UAV) especially for small UAV is challenging. A robust system should be able to not only detect obstacles but the free region for the avoidance path as well. Besides, the configuration of the obstacles in the operating environment should never be disregard. In this paper, expansion cues from the detected feature points with the help of convex hull will be used to categorize the regions in the image frame. A micro LIDAR sensor is used as the initial detector of obstacle and queue for image capturing by the camera. Next, ORB algorithm is applied to find the obstacle regions and free space regions. This is done through the principal of object size changes and distance relationship in an image perspective. The proposed system was evaluated through series of experiments in a real environment which consist of different configuration of obstacles. The experiments show the proposed system was able to find the safe avoidance region regardless of the configuration of the obstacles in the operating environment. Keywords: Expansion cue; ORB; Feature points; Safe avoidance region
利用特征点扩展线索检测无人飞行器的安全避让区域
为无人驾驶飞行器(UAV),尤其是小型无人驾驶飞行器开发障碍物探测系统是一项挑战。一个强大的系统不仅要能探测到障碍物,还要能探测到避障路径的自由区域。此外,操作环境中的障碍物配置也不容忽视。在本文中,将借助凸壳对检测到的特征点进行扩展,从而对图像帧中的区域进行分类。微型激光雷达传感器被用作障碍物的初始检测器和摄像头捕捉图像的队列。接着,应用 ORB 算法来寻找障碍物区域和自由空间区域。这是通过图像透视中物体大小变化和距离关系的原理来实现的。通过在由不同障碍物构成的真实环境中进行一系列实验,对所提出的系统进行了评估。实验结果表明,无论操作环境中的障碍物配置如何,所提出的系统都能找到安全的避让区域。关键词扩展提示;ORB;特征点;安全避让区域
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
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