Night-time indoor relocalization using depth image with Convolutional Neural Networks

Ruihao Li, Qiang Liu, Jianjun Gui, Dongbing Gu, Huosheng Hu
{"title":"Night-time indoor relocalization using depth image with Convolutional Neural Networks","authors":"Ruihao Li, Qiang Liu, Jianjun Gui, Dongbing Gu, Huosheng Hu","doi":"10.1109/ICONAC.2016.7604929","DOIUrl":null,"url":null,"abstract":"In this work, we present a Convolutional Neural Network(CNN) with depth images as its inputs to solve the relocalization problem of a moving platform in night-time indoor environment. The developed algorithm can estimate the camera pose in an end-to-end manner with 0.40m and 7.49° errors in real time during night. It does not require any geometric computation as it directly uses a CNN for 6 DOFs pose regression. The architecture and its encoding methods of depth images are discussed. The proposed method is also evaluated on benchmark datasets collected from a motion capture system in our lab.","PeriodicalId":375052,"journal":{"name":"2016 22nd International Conference on Automation and Computing (ICAC)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 22nd International Conference on Automation and Computing (ICAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICONAC.2016.7604929","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

In this work, we present a Convolutional Neural Network(CNN) with depth images as its inputs to solve the relocalization problem of a moving platform in night-time indoor environment. The developed algorithm can estimate the camera pose in an end-to-end manner with 0.40m and 7.49° errors in real time during night. It does not require any geometric computation as it directly uses a CNN for 6 DOFs pose regression. The architecture and its encoding methods of depth images are discussed. The proposed method is also evaluated on benchmark datasets collected from a motion capture system in our lab.
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
基于卷积神经网络的深度图像夜间室内定位
在这项工作中,我们提出了一种以深度图像为输入的卷积神经网络(CNN)来解决夜间室内环境中移动平台的重新定位问题。该算法可以在夜间实时估计相机姿态,误差为0.40m和7.49°。它不需要任何几何计算,因为它直接使用CNN进行6 dof姿态回归。讨论了深度图像的结构及其编码方法。该方法还在我们实验室的一个动作捕捉系统中收集的基准数据集上进行了评估。
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
Comparative study of Partial Discharge emulators for the calibration of Free-Space radiometric measurements Knowledge representation of large medical data using XML An investigation of electrical motor parameters in a sensorless variable speed drive for machine fault diagnosis A novel fault-tolerant control strategy for Near Space Hypersonic Vehicles via Least Squares Support Vector Machine and Backstepping method Automatic text summarization using fuzzy inference
×
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