Bluetooth Phased-array Aided Inertial Navigation Using Factor Graphs: Experimental Verification

TL;DR

This paper evaluates Bluetooth phased-array systems for inertial navigation in GNSS-denied environments, using factor graph optimization and experimental drone data to compare estimation strategies.

Contribution

It introduces a robust factor graph-based estimation approach for Bluetooth-aided inertial navigation using commercial hardware in challenging scenarios.

Findings

Bluetooth angular measurements improve navigation accuracy in GNSS-denied conditions.

Factor graph optimization effectively fuses Bluetooth, range, and barometric data.

Experimental results validate the proposed navigation method on drone flights.

Abstract

Phased-array Bluetooth systems have emerged as a low-cost alternative for performing aided inertial navigation in GNSS-denied use cases such as warehouse logistics, drone landings, and autonomous docking. Basing a navigation system off of commercial-off-the-shelf components may reduce the barrier of entry for phased-array radio navigation systems, albeit at the cost of significantly noisier measurements and relatively short feasible range. In this paper, we compare robust estimation strategies for a factor graph optimisation-based estimator using experimental data collected from multirotor drone flight. We evaluate performance in loss-of-GNSS scenarios when aided by Bluetooth angular measurements, as well as range or barometric pressure.