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Related Experiment Video

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A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

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Published on: September 30, 2019

Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints.

Peter Sarcevic¹, Dominik Csik^1,2, Akos Odry¹

¹Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, Moszkvai krt. 9, 6725 Szeged, Hungary.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This study introduces a new indoor positioning method for mobile robots by fusing Received Signal Strength Indicator (RSSI) and magnetometer data. This fusion significantly improves 2D localization accuracy by over 35% compared to using either method alone.

Keywords:

RSSI fingerprint indoor positioning localization magnetometer mobile robots position estimation sensor fusion

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Last Updated: Aug 8, 2025

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Area of Science:

Robotics
Indoor Localization
Sensor Fusion

Background:

Received Signal Strength Indicator (RSSI)-based fingerprinting is common for indoor localization but prone to high error rates.
Magnetic field disturbances offer stable measurements, gaining traction for indoor localization.

Purpose of the Study:

To propose a novel fingerprinting-based indoor 2D positioning method for mobile robots.
To fuse Received Signal Strength Indicator (RSSI) and magnetometer measurements for enhanced localization accuracy.

Main Methods:

Utilized multilayer perceptron (MLP) feedforward neural networks for 2D position determination.
Fused RSSI values from anchor nodes with mobile node magnetic field measurements.
Investigated magnetic field measurements using sensor axes, magnitude, and combined approaches.

Main Results:

Significant improvements in localization accuracy were achieved by fusing RSSI and magnetometer data.
Accuracy gains exceeded 35% in scenarios with high magnetic field variance compared to single-sensor methods.
Validation conducted in two realistic indoor environments with static objects.

Conclusions:

The fusion of RSSI and magnetometer data presents a robust solution for indoor 2D localization in mobile robots.
The proposed method effectively mitigates errors associated with traditional RSSI-based techniques.
This approach offers a substantial performance enhancement for indoor navigation systems.