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Exploiting Radio Fingerprints for Simultaneous Localization and Mapping

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arxiv 2305.13635 v1 pith:RF3UG2WR submitted 2023-05-23 cs.RO

classification cs.RO
keywords radioslamfingerprintslocalizationmappingenvironmentenvironmentsfingerprint
verification ladder T0 review T1 audit T2 compute T3 formal
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Simultaneous localization and mapping (SLAM) is paramount for unmanned systems to achieve self-localization and navigation. It is challenging to perform SLAM in large environments, due to sensor limitations, complexity of the environment, and computational resources. We propose a novel approach for localization and mapping of autonomous vehicles using radio fingerprints, for example WiFi (Wireless Fidelity) or LTE (Long Term Evolution) radio features, which are widely available in the existing infrastructure. In particular, we present two solutions to exploit the radio fingerprints for SLAM. In the first solution-namely Radio SLAM, the output is a radio fingerprint map generated using SLAM technique. In the second solution-namely Radio+LiDAR SLAM, we use radio fingerprint to assist conventional LiDAR-based SLAM to improve accuracy and speed, while generating the occupancy map. We demonstrate the effectiveness of our system in three different environments, namely outdoor, indoor building, and semi-indoor environment.

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    cs.CL 2025-07 reject novelty 3.0 of 10

    AutoRAG-LoRA reports a 46.6% relative reduction in classifier-flagged hallucinations on TruthfulQA, but the evaluation uses the same classifier that triggers the corrective training.

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