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

Glen Hjelmerud Mørkbak Sørensen, Torleiv H. Bryne, Kristoffer Gryte, Tor Arne Johansen

TL;DR

This paper compares robust estimation strategies for a factor graph optimisation-based estimator using experimental data collected from multirotor drone flight, and evaluates performance in loss-of-GNSS scenarios when aided by Bluetooth angular measurements, as well as range or barometric pressure.

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.

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

TL;DR

This paper compares robust estimation strategies for a factor graph optimisation-based estimator using experimental data collected from multirotor drone flight, and evaluates performance in loss-of-GNSS scenarios when aided by Bluetooth angular measurements, as well as range or barometric pressure.

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.
Paper Structure (16 sections, 12 equations, 5 figures, 2 tables)

This paper contains 16 sections, 12 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Notation and coordinate frames
  4. SE(3) matrix Lie group theory
  5. Bluetooth LE direction finding
  6. Inertial Navigation system
  7. On-manifold IMU preintegration factor
  8. GNSS position and attitude factors
  9. Bluetooth factors
  10. Barometric factor
  11. Propagating the estimate and dealing with outliers
  12. Experiment setup
  13. Results and discussion
  14. BLE angular measurements and RTK range
  15. BLE angular measurements and barometric pressure
  16. ...and 1 more sections

Figures (5)

  • Figure 1: Multirotor drone and ground station in the field. The Bluetooth antenna array (green) is mounted flat.
  • Figure 2: NED position computed from measurements.
  • Figure 3: Estimator performance without mitigation of BLE PARS outliers. Note the shaded sections roughly segmenting the data based on RTK/PARS and the manoeuvre performed during the given time interval.
  • Figure 4: Estimation error in handover from RTK to BLE angular measurements and RTK range. $3\sigma$-bounds are given by dashed lines of the same colour.
  • Figure 5: Estimation error in handover from RTK to BLE angular measurements and barometric pressure. $3\sigma$-bounds are given by dashed lines of the same colour.