Stereo Visual Odometry for Indoor Localization of Ship Model

Mario Kučić, M. Valčić
{"title":"Stereo Visual Odometry for Indoor Localization of Ship Model","authors":"Mario Kučić, M. Valčić","doi":"10.18048/2020.58.04","DOIUrl":null,"url":null,"abstract":"Typically, ships are designed for open sea navigation and thus research of autonomous ships is mostly done for that particular area. This paper explores the possibility of using low-cost sensors for localization inside the small navigation area. The localization system is based on the technology used for developing autonomous cars. The main part of the system is visual odometry using stereo cameras fused with Inertial Measurement Unit (IMU) data coupled with Kalman and particle filters to get decimetre level accuracy inside a basin for different surface conditions. The visual odometry uses cropped frames for stereo cameras and Good features to track algorithm for extracting features to get depths for each feature that is used for estimation of ship model movement. Experimental results showed that the proposed system could localize itself within a decimetre accuracy implying that there is a real possibility for ships in using visual odometry for autonomous navigation on narrow waterways, which can have a significant impact on future transportation.","PeriodicalId":366194,"journal":{"name":"Journal of Maritime & Transportation Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Maritime & Transportation Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18048/2020.58.04","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

Typically, ships are designed for open sea navigation and thus research of autonomous ships is mostly done for that particular area. This paper explores the possibility of using low-cost sensors for localization inside the small navigation area. The localization system is based on the technology used for developing autonomous cars. The main part of the system is visual odometry using stereo cameras fused with Inertial Measurement Unit (IMU) data coupled with Kalman and particle filters to get decimetre level accuracy inside a basin for different surface conditions. The visual odometry uses cropped frames for stereo cameras and Good features to track algorithm for extracting features to get depths for each feature that is used for estimation of ship model movement. Experimental results showed that the proposed system could localize itself within a decimetre accuracy implying that there is a real possibility for ships in using visual odometry for autonomous navigation on narrow waterways, which can have a significant impact on future transportation.
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
船舶模型室内定位的立体视觉里程计
通常情况下,船舶是为远海航行而设计的,因此自主船舶的研究主要针对该特定区域进行。本文探讨了在小导航区域内使用低成本传感器进行定位的可能性。定位系统是以开发自动驾驶汽车的技术为基础的。该系统的主要部分是视觉测程,使用融合惯性测量单元(IMU)数据的立体摄像机,结合卡尔曼滤波和粒子滤波,在不同的表面条件下获得盆地内分米级的精度。视觉里程计使用立体摄像机的裁剪帧和Good特征跟踪算法来提取特征,以获得用于估计船舶模型运动的每个特征的深度。实验结果表明,所提出的系统可以将自身定位在分米精度范围内,这意味着船舶有可能在狭窄的水道上使用视觉里程计进行自主导航,这可能对未来的运输产生重大影响。
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
Use of Convolutional Neural Network for Fish Species Classification Projection of the Electronic Toll Collection System in the Republic of Croatia The Negative Impact of the Cruising Industry on the Environment An Overview of Modern Technologies in Leading Global Seaports Maritime Challenges in Crisis Times
×
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