TuRF: Fast Data Collection for Fingerprint-based Indoor Localization

TL;DR

TuRF is a rapid, path-based fingerprint collection method for indoor localization that reduces survey time by leveraging walking paths and step counting, maintaining high localization accuracy.

Contribution

Introduces TuRF, a fast fingerprint collection technique using walking paths and step counting, simplifying site surveys for indoor positioning systems.

Findings

Significantly reduces site survey time.

Maintains high localization accuracy.

Effective during walking along predefined paths.

Abstract

Many infrastructure-free indoor positioning systems rely on fine-grained location-dependent fingerprints to train models for localization. The site survey process to collect fingerprints is laborious and is considered one of the major obstacles to deploying such systems. In this paper, we propose TuRF, a fast path-based fingerprint collection mechanism for site survey. We demonstrate the feasibility to collect fingerprints for indoor localization during walking along predefined paths. A step counter is utilized to accommodate the variations in walking speed. Approximate location labels inferred from the steps are then used to train a Gaussian Process regression model. Extensive experiments show that TuRF can significantly reduce the required time for site survey, without compromising the localization performance.