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

Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity