SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot

Xinyu Gao; Yuanwei Liu; Xidong Mu

doi:10.1109/ICCWorkshops50388.2021.9473676

SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot

Xinyu Gao
, Yuanwei Liu
, Xidong Mu

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

10 Citations (Scopus)

Abstract

A novel simultaneous localization and radio mapping (SLARM) framework for communication-aware connected robots in the unknown indoor environment is proposed, where the simultaneous localization and mapping (SLAM) algorithm and the global geographic map recovery (GGMR) algorithm are leveraged to simultaneously construct a geographic map and a radio map named a channel power gain map. Specifically, the geographic map contains the information of a precise layout of obstacles and passable regions, and the radio map characterizes the position-dependent maximum expected channel power gain between the access point and the connected robot. Numerical results show that: 1) The pre-defined resolution in the SLAM algorithm and the proposed GGMR algorithm significantly affect the accuracy of the constructed radio map; and 2) The accuracy of radio map constructed by the SLARM framework is more than 78.78% when the resolution value smaller than 0.15m, and the accuracy reaches 91.95% when the resolution value is pre-defined as 0.05m.

Original language	English
Title of host publication	2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728194417
DOIs	https://doi.org/10.1109/ICCWorkshops50388.2021.9473676
Publication status	Published - 09 Jun 2021
Externally published	Yes
Event	2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Virtual, Online Duration: 14 Jun 2021 → 23 Jun 2021

Publication series

Name	2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings
Publisher	IEEE
ISSN (Print)	2164-7038
ISSN (Electronic)	2694-2941

Conference

Conference	2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021
City	Virtual, Online
Period	14/06/2021 → 23/06/2021

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Hardware and Architecture
Information Systems and Management

Access to Document

10.1109/ICCWorkshops50388.2021.9473676Licence: Unspecified

Cite this

Gao, X., Liu, Y., & Mu, X. (2021). SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot. In 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings Article 9473676 (2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCWorkshops50388.2021.9473676

Gao, Xinyu ; Liu, Yuanwei ; Mu, Xidong. / SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot. 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings).

@inproceedings{04a7ed4f698e44538ee1c69bbb98161f,

title = "SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot",

abstract = "A novel simultaneous localization and radio mapping (SLARM) framework for communication-aware connected robots in the unknown indoor environment is proposed, where the simultaneous localization and mapping (SLAM) algorithm and the global geographic map recovery (GGMR) algorithm are leveraged to simultaneously construct a geographic map and a radio map named a channel power gain map. Specifically, the geographic map contains the information of a precise layout of obstacles and passable regions, and the radio map characterizes the position-dependent maximum expected channel power gain between the access point and the connected robot. Numerical results show that: 1) The pre-defined resolution in the SLAM algorithm and the proposed GGMR algorithm significantly affect the accuracy of the constructed radio map; and 2) The accuracy of radio map constructed by the SLARM framework is more than 78.78\% when the resolution value smaller than 0.15m, and the accuracy reaches 91.95\% when the resolution value is pre-defined as 0.05m.",

author = "Xinyu Gao and Yuanwei Liu and Xidong Mu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 ; Conference date: 14-06-2021 Through 23-06-2021",

year = "2021",

month = jun,

day = "9",

doi = "10.1109/ICCWorkshops50388.2021.9473676",

language = "English",

series = "2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings",

address = "United States",

}

Gao, X, Liu, Y & Mu, X 2021, SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot. in 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings., 9473676, 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021, Virtual, Online, 14/06/2021. https://doi.org/10.1109/ICCWorkshops50388.2021.9473676

SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot. / Gao, Xinyu; Liu, Yuanwei; Mu, Xidong.
2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. 9473676 (2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings).

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

TY - GEN

T1 - SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot

AU - Gao, Xinyu

AU - Liu, Yuanwei

AU - Mu, Xidong

PY - 2021/6/9

Y1 - 2021/6/9

N2 - A novel simultaneous localization and radio mapping (SLARM) framework for communication-aware connected robots in the unknown indoor environment is proposed, where the simultaneous localization and mapping (SLAM) algorithm and the global geographic map recovery (GGMR) algorithm are leveraged to simultaneously construct a geographic map and a radio map named a channel power gain map. Specifically, the geographic map contains the information of a precise layout of obstacles and passable regions, and the radio map characterizes the position-dependent maximum expected channel power gain between the access point and the connected robot. Numerical results show that: 1) The pre-defined resolution in the SLAM algorithm and the proposed GGMR algorithm significantly affect the accuracy of the constructed radio map; and 2) The accuracy of radio map constructed by the SLARM framework is more than 78.78% when the resolution value smaller than 0.15m, and the accuracy reaches 91.95% when the resolution value is pre-defined as 0.05m.

AB - A novel simultaneous localization and radio mapping (SLARM) framework for communication-aware connected robots in the unknown indoor environment is proposed, where the simultaneous localization and mapping (SLAM) algorithm and the global geographic map recovery (GGMR) algorithm are leveraged to simultaneously construct a geographic map and a radio map named a channel power gain map. Specifically, the geographic map contains the information of a precise layout of obstacles and passable regions, and the radio map characterizes the position-dependent maximum expected channel power gain between the access point and the connected robot. Numerical results show that: 1) The pre-defined resolution in the SLAM algorithm and the proposed GGMR algorithm significantly affect the accuracy of the constructed radio map; and 2) The accuracy of radio map constructed by the SLARM framework is more than 78.78% when the resolution value smaller than 0.15m, and the accuracy reaches 91.95% when the resolution value is pre-defined as 0.05m.

U2 - 10.1109/ICCWorkshops50388.2021.9473676

DO - 10.1109/ICCWorkshops50388.2021.9473676

M3 - Conference contribution

AN - SCOPUS:85112855129

T3 - 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings

BT - 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021

Y2 - 14 June 2021 through 23 June 2021

ER -

Gao X, Liu Y, Mu X. SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot. In 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. 9473676. (2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings). doi: 10.1109/ICCWorkshops50388.2021.9473676

SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this