基于外观的分组LASSO回归定位与室内实验

Huan N. Do, Jongeun Choi, C. Lim, T. Maiti
{"title":"基于外观的分组LASSO回归定位与室内实验","authors":"Huan N. Do, Jongeun Choi, C. Lim, T. Maiti","doi":"10.1109/AIM.2015.7222667","DOIUrl":null,"url":null,"abstract":"This paper proposes appearance-based localization using online vision images collected from an omnidirectional camera attached on a mobile robot or a vehicle. Our approach builds on a combination of the group Least Absolute Shrinkage and Selection Operator (LASSO) and the extended Kalman filter (EKF). Fast Fourier transform (FFT) and Histogram are extracted from omni-directional images, the features of which are selected via the group LASSO regression. The EKF takes the output of the group LASSO regression based first-stage localization as the observation. The indoor experimental results demonstrate the effectiveness of our approach.","PeriodicalId":199432,"journal":{"name":"2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Appearance-based localization using Group LASSO regression with an indoor experiment\",\"authors\":\"Huan N. Do, Jongeun Choi, C. Lim, T. Maiti\",\"doi\":\"10.1109/AIM.2015.7222667\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes appearance-based localization using online vision images collected from an omnidirectional camera attached on a mobile robot or a vehicle. Our approach builds on a combination of the group Least Absolute Shrinkage and Selection Operator (LASSO) and the extended Kalman filter (EKF). Fast Fourier transform (FFT) and Histogram are extracted from omni-directional images, the features of which are selected via the group LASSO regression. The EKF takes the output of the group LASSO regression based first-stage localization as the observation. The indoor experimental results demonstrate the effectiveness of our approach.\",\"PeriodicalId\":199432,\"journal\":{\"name\":\"2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIM.2015.7222667\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIM.2015.7222667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

本文提出了一种基于外观的定位方法,使用从移动机器人或车辆上的全向相机收集的在线视觉图像。我们的方法建立在最小绝对收缩和选择算子(LASSO)和扩展卡尔曼滤波器(EKF)的组合之上。从全向图像中提取快速傅里叶变换(FFT)和直方图,并通过分组LASSO回归选择特征。EKF将基于分组LASSO回归的第一阶段定位的输出作为观测值。室内实验结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Appearance-based localization using Group LASSO regression with an indoor experiment
This paper proposes appearance-based localization using online vision images collected from an omnidirectional camera attached on a mobile robot or a vehicle. Our approach builds on a combination of the group Least Absolute Shrinkage and Selection Operator (LASSO) and the extended Kalman filter (EKF). Fast Fourier transform (FFT) and Histogram are extracted from omni-directional images, the features of which are selected via the group LASSO regression. The EKF takes the output of the group LASSO regression based first-stage localization as the observation. The indoor experimental results demonstrate the effectiveness of our approach.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:604180095
Book学术官方微信