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

arXiv · 2110.06541

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

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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.

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BibTeX RIS

Ran Liu, Zhenghong Qin, Hua Zhang, Billy Pik Lik Lau, Khairuldanial Ismail, Achala Athukorala, Chau Yuen, Yong Liang Guan, U-Xuan Tan. 2021-10-13. Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity. https://arxiv.org/abs/2110.06541

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