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MAINS: A Magnetic Field Aided Inertial Navigation System for Indoor Positioning (2312.02599v3)

Published 5 Dec 2023 in cs.RO and eess.SP

Abstract: A Magnetic field Aided Inertial Navigation System (MAINS) for indoor navigation is proposed in this paper. MAINS leverages an array of magnetometers to measure spatial variations in the magnetic field, which are then used to estimate the displacement and orientation changes of the system, thereby aiding the inertial navigation system (INS). Experiments show that MAINS significantly outperforms the stand-alone INS, demonstrating a remarkable two orders of magnitude reduction in position error. Furthermore, when compared to the state-of-the-art magnetic-field-aided navigation approach, the proposed method exhibits slightly improved horizontal position accuracy. On the other hand, it has noticeably larger vertical error on datasets with large magnetic field variations. However, one of the main advantages of MAINS compared to the state-of-the-art is that it enables flexible sensor configurations. The experimental results show that the position error after 2 minutes of navigation in most cases is less than 3 meters when using an array of 30 magnetometers. Thus, the proposed navigation solution has the potential to solve one of the key challenges faced with current magnetic-field simultaneous localization and mapping (SLAM) solutions: the very limited allowable length of the exploration phase during which unvisited areas are mapped.

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Citations (2)
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Summary

  • The paper proposes MAINS as a magnetic field aided inertial navigation system that reduces position error by two orders of magnitude compared to pure INS.
  • It employs magnetometers to capture spatial variations in Earth’s magnetic field and integrates this data with inertial measurements for enhanced accuracy.
  • Experimental validation shows that MAINS achieves sub-three meter error after two minutes of GPS-denied navigation.

Understanding MAINS: The Magnetic Field Aided Inertial Navigation System

The Problem with Indoor Positioning

Indoor positioning is a crucial aspect of modern navigation systems, especially as we become more reliant on technology to guide us through complex buildings like airports, malls, and warehouses. However, conventional GPS-based methods, which work seamlessly outdoors, are ineffective indoors due to the lack of satellite signals. This paper discusses an innovative approach to overcome this limitation by utilizing magnetic fields within buildings.

The MAINS Solution

The proposed solution, termed MAINS, stands for Magnetic field Aided Inertial Navigation System. MAINS uses a collection of magnetometers, which are instruments that measure magnetic fields, to track spatial variations in the Earth's magnetic field. This information, when combined with an inertial navigation system (INS), substantially enhances the estimation of displacement and orientation changes of a moving object.

MAINS has been tested and has achieved a remarkable two orders of magnitude reduction in position error compared to a pure INS. Moreover, MAINS slightly improves horizontal position accuracy over the state-of-the-art methods.

Advantages and Experimental Validation

One of the highlights of MAINS is its flexible sensor configuration. This adaptability was demonstrated in experiments showing that in most scenarios, after two minutes of navigation without GPS aid, the position error remained under three meters, which is significant considering the technology it is compared against. These experiments also addressed a key challenge in magnetic field simultaneous localization and mapping (SLAM), which is the mapping phase when exploring unvisited areas. MAINS extends this exploration phase far beyond current limitations when using low-cost inertial sensors.

The paper doesn't neglect the imperfections inherent in magnetic field modeling. It proposes a methodology to update the magnetic field model's coefficients dynamically based on the movement and orientation changes of the sensor platform, considering the associated errors and disturbances.

Performance Assessment and Future Directions

The performance of MAINS was assessed using real-world data. The algorithm was benchmarked against the latest methods and showcased superior precision in estimating positions indoors. Furthermore, the data and code used in the experiments have been made publicly available, which can help further research in the area of magnetic-field-based positioning.

As future work, the paper suggests exploring loop closure detection mechanisms for magnetic field SLAM and unifying sensor calibration within the MAINS framework. Additionally, there is a scope of understanding the impact of magnetic field models on positioning errors more precisely.

MAINS stands out as an innovative method that leverages the Earth’s magnetic field to enhance indoor navigation systems, showcasing a solution that is not only promising in terms of accuracy but also adaptable in its application. As the technology matures, we may soon see MAINS integrated into a myriad of indoor positioning systems, guiding us with unprecedented reliability.

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