Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity

TL;DR

This paper introduces a collaborative WiFi-based SLAM method for multiple robots, leveraging radio fingerprints and a novel similarity model to improve localization accuracy in large environments.

Contribution

The paper proposes a centralized multi-robot SLAM framework using WiFi radio fingerprints and a new similarity model combining RSS and detection likelihood.

Findings

The similarity model improves localization accuracy.

Collaborative SLAM outperforms single-robot approaches.

Extensive experiments validate the effectiveness of the method.

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.