Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity

Ran Liu; Zhenghong Qin; Hua Zhang; Billy Pik Lik Lau; Khairuldanial Ismail; Achala Athukorala; Chau Yuen; Yong Liang Guan; U. Xuan Tan

doi:10.1109/ROBIO54168.2021.9739222

Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity

Ran Liu^*, Zhenghong Qin, Hua Zhang, Billy Pik Lik Lau, Khairuldanial Ismail, Achala Athukorala, Chau Yuen, Yong Liang Guan, U. Xuan Tan

^*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

Simultaneous Localization and Mapping (SLAM) enables autonomous robots to navigate and execute their tasks through unknown environments. However, performing SLAM in large environments with a single robot is not efficient, and visual or LiDAR-based SLAM requires feature extraction and matching algorithms, which are computationally expensive. In this paper, we present a collaborative SLAM approach with multiple robots using the pervasive WiFi radio signals. A centralized solution is proposed to optimize the trajectory based on the odometry and radio fingerprints collected from multiple robots. To improve the localization accuracy, a novel similarity model is introduced that combines received signal strength (RSS) and detection likelihood of an access point (AP). We perform extensive experiments to demonstrate the effectiveness of the proposed similarity model and collaborative SLAM framework.

Original language	English
Title of host publication	2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	795-801
Number of pages	7
ISBN (Electronic)	9781665405355
DOIs	https://doi.org/10.1109/ROBIO54168.2021.9739222
Publication status	Published - 2021
Externally published	Yes
Event	2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021 - Sanya, China Duration: Dec 27 2021 → Dec 31 2021

Publication series

Name	2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021

Conference

Conference	2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
Country/Territory	China
City	Sanya
Period	12/27/21 → 12/31/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

ASJC Scopus Subject Areas

Artificial Intelligence
Mechanical Engineering
Control and Optimization

Access to Document

10.1109/ROBIO54168.2021.9739222

Cite this

Liu, R., Qin, Z., Zhang, H., Lau, B. P. L., Ismail, K., Athukorala, A., Yuen, C., Guan, Y. L., & Tan, U. X. (2021). Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity. In 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021 (pp. 795-801). (2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROBIO54168.2021.9739222

@inproceedings{72b7d000717b4e9ea306b3752366a0e9,

title = "Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity",

abstract = "Simultaneous Localization and Mapping (SLAM) enables autonomous robots to navigate and execute their tasks through unknown environments. However, performing SLAM in large environments with a single robot is not efficient, and visual or LiDAR-based SLAM requires feature extraction and matching algorithms, which are computationally expensive. In this paper, we present a collaborative SLAM approach with multiple robots using the pervasive WiFi radio signals. A centralized solution is proposed to optimize the trajectory based on the odometry and radio fingerprints collected from multiple robots. To improve the localization accuracy, a novel similarity model is introduced that combines received signal strength (RSS) and detection likelihood of an access point (AP). We perform extensive experiments to demonstrate the effectiveness of the proposed similarity model and collaborative SLAM framework.",

author = "Ran Liu and Zhenghong Qin and Hua Zhang and Lau, {Billy Pik Lik} and Khairuldanial Ismail and Achala Athukorala and Chau Yuen and Guan, {Yong Liang} and Tan, {U. Xuan}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021 ; Conference date: 27-12-2021 Through 31-12-2021",

year = "2021",

doi = "10.1109/ROBIO54168.2021.9739222",

language = "English",

series = "2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021",

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

pages = "795--801",

booktitle = "2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021",

address = "United States",

}

Liu, R, Qin, Z, Zhang, H, Lau, BPL, Ismail, K, Athukorala, A, Yuen, C, Guan, YL & Tan, UX 2021, Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity. in 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021. 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021, Institute of Electrical and Electronics Engineers Inc., pp. 795-801, 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021, Sanya, China, 12/27/21. https://doi.org/10.1109/ROBIO54168.2021.9739222

Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity. / Liu, Ran; Qin, Zhenghong; Zhang, Hua et al.
2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 795-801 (2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021).

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

TY - GEN

T1 - Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity

AU - Liu, Ran

AU - Qin, Zhenghong

AU - Zhang, Hua

AU - Lau, Billy Pik Lik

AU - Ismail, Khairuldanial

AU - Athukorala, Achala

AU - Yuen, Chau

AU - Guan, Yong Liang

AU - Tan, U. Xuan

PY - 2021

Y1 - 2021

N2 - Simultaneous Localization and Mapping (SLAM) enables autonomous robots to navigate and execute their tasks through unknown environments. However, performing SLAM in large environments with a single robot is not efficient, and visual or LiDAR-based SLAM requires feature extraction and matching algorithms, which are computationally expensive. In this paper, we present a collaborative SLAM approach with multiple robots using the pervasive WiFi radio signals. A centralized solution is proposed to optimize the trajectory based on the odometry and radio fingerprints collected from multiple robots. To improve the localization accuracy, a novel similarity model is introduced that combines received signal strength (RSS) and detection likelihood of an access point (AP). We perform extensive experiments to demonstrate the effectiveness of the proposed similarity model and collaborative SLAM framework.

AB - Simultaneous Localization and Mapping (SLAM) enables autonomous robots to navigate and execute their tasks through unknown environments. However, performing SLAM in large environments with a single robot is not efficient, and visual or LiDAR-based SLAM requires feature extraction and matching algorithms, which are computationally expensive. In this paper, we present a collaborative SLAM approach with multiple robots using the pervasive WiFi radio signals. A centralized solution is proposed to optimize the trajectory based on the odometry and radio fingerprints collected from multiple robots. To improve the localization accuracy, a novel similarity model is introduced that combines received signal strength (RSS) and detection likelihood of an access point (AP). We perform extensive experiments to demonstrate the effectiveness of the proposed similarity model and collaborative SLAM framework.

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Liu R, Qin Z, Zhang H, Lau BPL, Ismail K, Athukorala A et al. Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity. In 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 795-801. (2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021). doi: 10.1109/ROBIO54168.2021.9739222

Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity

Abstract

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Conference

Bibliographical note

ASJC Scopus Subject Areas

Access to Document

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