基于ROS的环境建模及带航位推算和不确定性的移动机器人位置估计研究

H. Ahmad, Mohammad Heerwan Peeie, M. S. Ramli, Amir Akramin Bin Shafie, M. Rahiman
{"title":"基于ROS的环境建模及带航位推算和不确定性的移动机器人位置估计研究","authors":"H. Ahmad, Mohammad Heerwan Peeie, M. S. Ramli, Amir Akramin Bin Shafie, M. Rahiman","doi":"10.1109/IEACon51066.2021.9654762","DOIUrl":null,"url":null,"abstract":"This paper aims to investigate Robot Operating System (ROS) based environment modelling and mobile robot position estimation considering dead reckoning and uncertainties. A mobile robot movement is analyzed in a few environment conditions by using Extended Kalman Filter with ROS to identify and examined the mobile robot estimation performance on its surroundings. The heading angle and initial state covariance performance are assessed with different mobile robot movement. The paper is organized mainly to describe the results from both simulation and experiment using Extended Kalman Filter that consists of undetermined and unpredictable environment states. For experimental verification, a Turtlebot3 equipped with a 360-degree LiDAR and IMU is being applied to demonstrate the performance of estimation in a situation that has unknown uncertainties in several conditions. Both simulation and experimental results indicates that state covariance is converging lesser than the initial state covariance in any environmental cases which is in contrast with the literatures. Besides, it is also found that the mobile robot heading angle is important to be accurate at all times for better estimation results.","PeriodicalId":397039,"journal":{"name":"2021 IEEE Industrial Electronics and Applications Conference (IEACon)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigation of ROS Based Environment Modelling and Mobile Robot Position Estimation with Dead Reckoning and Uncertainties\",\"authors\":\"H. Ahmad, Mohammad Heerwan Peeie, M. S. Ramli, Amir Akramin Bin Shafie, M. Rahiman\",\"doi\":\"10.1109/IEACon51066.2021.9654762\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to investigate Robot Operating System (ROS) based environment modelling and mobile robot position estimation considering dead reckoning and uncertainties. A mobile robot movement is analyzed in a few environment conditions by using Extended Kalman Filter with ROS to identify and examined the mobile robot estimation performance on its surroundings. The heading angle and initial state covariance performance are assessed with different mobile robot movement. The paper is organized mainly to describe the results from both simulation and experiment using Extended Kalman Filter that consists of undetermined and unpredictable environment states. For experimental verification, a Turtlebot3 equipped with a 360-degree LiDAR and IMU is being applied to demonstrate the performance of estimation in a situation that has unknown uncertainties in several conditions. Both simulation and experimental results indicates that state covariance is converging lesser than the initial state covariance in any environmental cases which is in contrast with the literatures. Besides, it is also found that the mobile robot heading angle is important to be accurate at all times for better estimation results.\",\"PeriodicalId\":397039,\"journal\":{\"name\":\"2021 IEEE Industrial Electronics and Applications Conference (IEACon)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE Industrial Electronics and Applications Conference (IEACon)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEACon51066.2021.9654762\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Industrial Electronics and Applications Conference (IEACon)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEACon51066.2021.9654762","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文旨在研究基于机器人操作系统(ROS)的环境建模和考虑航位推算和不确定性的移动机器人位置估计。利用带有ROS的扩展卡尔曼滤波对移动机器人在几种环境条件下的运动进行了分析,并检验了移动机器人对周围环境的估计性能。对不同移动机器人运动时的航向角和初始状态协方差性能进行了评价。本文主要描述了由不确定和不可预测的环境状态组成的扩展卡尔曼滤波器的仿真和实验结果。为了进行实验验证,正在应用配备360度激光雷达和IMU的Turtlebot3来演示在几种情况下具有未知不确定性的情况下的估计性能。仿真和实验结果均表明,在任何环境情况下,状态协方差的收敛速度都小于初始状态协方差。此外,还发现移动机器人的航向角对于始终保持准确是很重要的,以获得更好的估计结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of ROS Based Environment Modelling and Mobile Robot Position Estimation with Dead Reckoning and Uncertainties
This paper aims to investigate Robot Operating System (ROS) based environment modelling and mobile robot position estimation considering dead reckoning and uncertainties. A mobile robot movement is analyzed in a few environment conditions by using Extended Kalman Filter with ROS to identify and examined the mobile robot estimation performance on its surroundings. The heading angle and initial state covariance performance are assessed with different mobile robot movement. The paper is organized mainly to describe the results from both simulation and experiment using Extended Kalman Filter that consists of undetermined and unpredictable environment states. For experimental verification, a Turtlebot3 equipped with a 360-degree LiDAR and IMU is being applied to demonstrate the performance of estimation in a situation that has unknown uncertainties in several conditions. Both simulation and experimental results indicates that state covariance is converging lesser than the initial state covariance in any environmental cases which is in contrast with the literatures. Besides, it is also found that the mobile robot heading angle is important to be accurate at all times for better estimation results.
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:481959085
Book学术官方微信