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• W3205125896 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." @default.
• W3205125896 created "2021-10-25" @default.
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• W3205125896 date "2021-12-27" @default.
• W3205125896 modified "2023-09-30" @default.
• W3205125896 title "Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity" @default.
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• W3205125896 doi "https://doi.org/10.1109/robio54168.2021.9739222" @default.
• W3205125896 hasPublicationYear "2021" @default.
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