Development of an EKF-based localization algorithm using compass sensor and LRF

T. T. Hoang, P. M. Duong, N. T. T. Van, D. A. Viet, T. Q. Vinh
{"title":"Development of an EKF-based localization algorithm using compass sensor and LRF","authors":"T. T. Hoang, P. M. Duong, N. T. T. Van, D. A. Viet, T. Q. Vinh","doi":"10.1109/ICARCV.2012.6485182","DOIUrl":null,"url":null,"abstract":"This paper presents the implementation of a perceptual system for a mobile robot. The system is designed and installed with modern sensors and multi-point communication channels. The goal is to equip the robot with a high level of perception to support a wide range of navigating applications including Internet-based telecontrol, semi-autonomy, and autonomy. Due to uncertainties of acquiring data, a sensor fusion model is developed, in which heterogeneous measured data including odometry, compass heading and laser range is combined to get an optimal estimate in a statistical sense. The combination is carried out by an extended Kalman filter. Experimental results indicate that based on the system, the robot localization is enhanced and sufficient for navigation tasks.","PeriodicalId":441236,"journal":{"name":"2012 12th International Conference on Control Automation Robotics & Vision (ICARCV)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 12th International Conference on Control Automation Robotics & Vision (ICARCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCV.2012.6485182","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

Abstract

This paper presents the implementation of a perceptual system for a mobile robot. The system is designed and installed with modern sensors and multi-point communication channels. The goal is to equip the robot with a high level of perception to support a wide range of navigating applications including Internet-based telecontrol, semi-autonomy, and autonomy. Due to uncertainties of acquiring data, a sensor fusion model is developed, in which heterogeneous measured data including odometry, compass heading and laser range is combined to get an optimal estimate in a statistical sense. The combination is carried out by an extended Kalman filter. Experimental results indicate that based on the system, the robot localization is enhanced and sufficient for navigation tasks.
利用罗经传感器和LRF实现基于ekf的定位算法
本文介绍了一个移动机器人感知系统的实现。该系统设计并安装了现代传感器和多点通信信道。目标是为机器人配备高水平的感知能力,以支持广泛的导航应用,包括基于互联网的遥控、半自主和自主。针对采集数据的不确定性,提出了一种传感器融合模型,该模型将里程计、罗盘航向和激光距离等异构测量数据结合起来,在统计意义上得到最优估计。该组合由扩展卡尔曼滤波器实现。实验结果表明,基于该系统的机器人定位能力得到了增强,能够满足导航任务的需要。
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