Indoor navigation system using the fetch robot

Huishen Zhu; Brenton Leighton; Yongbo Chen; Xijun Ke; Songtao Liu; Liang Zhao

doi:10.1007/978-3-030-27538-9_59

Indoor navigation system using the fetch robot

Huishen Zhu, Brenton Leighton, Yongbo Chen^*, Xijun Ke, Songtao Liu, Liang Zhao

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

In this paper, we present a navigation system, including off-line mapping and on-line localization, for the Fetch robot in an indoor environment using Cartographer. This framework aims to build a practical, robust, and accurate Robot Operating System (ROS) package for the Fetch robot. Firstly, using Cartographer and the fusion of data from a laser scan and RGB-D camera, a two-dimensional (2D) off-line map is built. Then, the Adaptive Monte Carlo Localization (AMCL) ROS package is used to perform on-line localization. We use a simulation to validate this method of mapping and localization, then demonstrate our method live on the Fetch robot. A video about the simulation and experiment is shown in https://youtu.be/oOvxTOowe34.

Original language	English
Title of host publication	Intelligent Robotics and Applications
Subtitle of host publication	ICIRA 2019
Editors	Haibin Yu, Jinguo Liu, Lianqing Liu, Yuwang Liu, Zhaojie Ju, Dalin Zhou
Publisher	Springer
Pages	686-696
Number of pages	11
ISBN (Print)	9783030275372
DOIs	https://doi.org/10.1007/978-3-030-27538-9_59
Publication status	Published - 3 Aug 2019
Event	12th International Conference on Intelligent Robotics and Applications - Shenyang, China Duration: 8 Aug 2019 → 11 Aug 2019

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11743
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Intelligent Robotics and Applications
Abbreviated title	ICIRA 2019
Country/Territory	China
City	Shenyang
Period	8/08/19 → 11/08/19

Keywords / Materials (for Non-textual outputs)

data fusion
fetch robot
localization
mapping
ROS framework

Access to Document

10.1007/978-3-030-27538-9_59

Cite this

@inproceedings{f8ca0afcbe4f4eec8564620555cfe73c,

title = "Indoor navigation system using the fetch robot",

abstract = "In this paper, we present a navigation system, including off-line mapping and on-line localization, for the Fetch robot in an indoor environment using Cartographer. This framework aims to build a practical, robust, and accurate Robot Operating System (ROS) package for the Fetch robot. Firstly, using Cartographer and the fusion of data from a laser scan and RGB-D camera, a two-dimensional (2D) off-line map is built. Then, the Adaptive Monte Carlo Localization (AMCL) ROS package is used to perform on-line localization. We use a simulation to validate this method of mapping and localization, then demonstrate our method live on the Fetch robot. A video about the simulation and experiment is shown in https://youtu.be/oOvxTOowe34.",

keywords = "data fusion, fetch robot, localization, mapping, ROS framework",

author = "Huishen Zhu and Brenton Leighton and Yongbo Chen and Xijun Ke and Songtao Liu and Liang Zhao",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 ; Conference date: 08-08-2019 Through 11-08-2019",

year = "2019",

month = aug,

day = "3",

doi = "10.1007/978-3-030-27538-9_59",

language = "English",

isbn = "9783030275372",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "686--696",

editor = "Haibin Yu and Jinguo Liu and Lianqing Liu and Yuwang Liu and Zhaojie Ju and Dalin Zhou",

booktitle = "Intelligent Robotics and Applications",

address = "United Kingdom",

}

Zhu, H, Leighton, B, Chen, Y, Ke, X, Liu, S & Zhao, L 2019, Indoor navigation system using the fetch robot. in H Yu, J Liu, L Liu, Y Liu, Z Ju & D Zhou (eds), Intelligent Robotics and Applications: ICIRA 2019. Lecture Notes in Computer Science, vol. 11743, Springer, pp. 686-696, 12th International Conference on Intelligent Robotics and Applications, Shenyang, China, 8/08/19. https://doi.org/10.1007/978-3-030-27538-9_59

Indoor navigation system using the fetch robot. / Zhu, Huishen; Leighton, Brenton; Chen, Yongbo et al.
Intelligent Robotics and Applications: ICIRA 2019. ed. / Haibin Yu; Jinguo Liu; Lianqing Liu; Yuwang Liu; Zhaojie Ju; Dalin Zhou. Springer, 2019. p. 686-696 (Lecture Notes in Computer Science; Vol. 11743).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Indoor navigation system using the fetch robot

AU - Zhu, Huishen

AU - Leighton, Brenton

AU - Chen, Yongbo

AU - Ke, Xijun

AU - Liu, Songtao

AU - Zhao, Liang

PY - 2019/8/3

Y1 - 2019/8/3

N2 - In this paper, we present a navigation system, including off-line mapping and on-line localization, for the Fetch robot in an indoor environment using Cartographer. This framework aims to build a practical, robust, and accurate Robot Operating System (ROS) package for the Fetch robot. Firstly, using Cartographer and the fusion of data from a laser scan and RGB-D camera, a two-dimensional (2D) off-line map is built. Then, the Adaptive Monte Carlo Localization (AMCL) ROS package is used to perform on-line localization. We use a simulation to validate this method of mapping and localization, then demonstrate our method live on the Fetch robot. A video about the simulation and experiment is shown in https://youtu.be/oOvxTOowe34.

AB - In this paper, we present a navigation system, including off-line mapping and on-line localization, for the Fetch robot in an indoor environment using Cartographer. This framework aims to build a practical, robust, and accurate Robot Operating System (ROS) package for the Fetch robot. Firstly, using Cartographer and the fusion of data from a laser scan and RGB-D camera, a two-dimensional (2D) off-line map is built. Then, the Adaptive Monte Carlo Localization (AMCL) ROS package is used to perform on-line localization. We use a simulation to validate this method of mapping and localization, then demonstrate our method live on the Fetch robot. A video about the simulation and experiment is shown in https://youtu.be/oOvxTOowe34.

KW - data fusion

KW - fetch robot

KW - localization

KW - mapping

KW - ROS framework

UR - http://www.scopus.com/inward/record.url?scp=85070493229&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-27538-9_59

DO - 10.1007/978-3-030-27538-9_59

M3 - Conference contribution

AN - SCOPUS:85070493229

SN - 9783030275372

T3 - Lecture Notes in Computer Science

SP - 686

EP - 696

BT - Intelligent Robotics and Applications

A2 - Yu, Haibin

A2 - Liu, Jinguo

A2 - Liu, Lianqing

A2 - Liu, Yuwang

A2 - Ju, Zhaojie

A2 - Zhou, Dalin

PB - Springer

T2 - 12th International Conference on Intelligent Robotics and Applications

Y2 - 8 August 2019 through 11 August 2019

ER -

Indoor navigation system using the fetch robot

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Keywords / Materials (for Non-textual outputs)

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