Improving Indoor Pedestrian Navigation Using Enhanced QMD and AIEZ Frameworks | HackerNoon

Table of Links

Abstract and I. Introduction

II. Methodology

III. Experimental Results

IV. Conclusion and Future Works, and References

III. EXPERIMENTAL RESULTS

The system consists of a consumer-grade IMU mounted on the heel of the shoe (as shown in Fig.3) with the transmission speed at 100 Hz via wireless mode to the laptop for logging and real-time visualization. The IMU named Mtw Awinda contains 3-axis MEMS accelerometer, 3-axis MEMS gyroscope and 3-axis MEMS magnetometer. The parameters in detail can be found in the website of Xsens company. Several experiments were performed to access the performance of the proposed methodology.

A. Walking in the Hard Magnetic Disturbance Field

In this case, a pedestrian walks along the corridor with the magnet placed on the floor as shown in Fig.5. The red circles in Fig.6 indicates that the hard magnetic disturbance happens due to the wrong magnetic heading while the INS heading keeps stable. Owing to the characteristic of the magnet, the magnetic field intensity near it is constant. Accordingly, from the zoomed drawing in Fig.6, the magnetic headings stay stable but wrong. The proposed QMD algorithm contributes to a right detection results compared with the classical QMD method and has a better performance.

B. Walking in Real Indoor Environment

In this case, we walked along the regular trajectory for three cycles for 7 minutes in the teaching building 26 in Tianjin University. The total distance of the first trajectory is about 515.5 meters. We evaluate the performance among the IEZ, IEZ+classical QMD and AIEZ (as shown in Fig.7) and logged the Total Travelled Distance (TTD) error in Table I.

IV. CONCLUSION AND FUTURE WORKS

In this paper, QMD was implemented to combine the EC algorithm and the HDR algorithm in order to achieve complementation between them. Classical QMD algorithm fails to recognize the hard magnetic field and mistakes it for pure magnetic field. Our proposed QMD method captures the moment when the magnetic field intensity is stable and the magnetic heading is similar to the INS heading. Hence, it have a better performance than classical QMD. AIEZ framework fuses the EC and HDR algorithm and is superior to IEZ and IEZ+classical QMD frameworks. In the future, plenty of experiments need to be settled to evaluate the performance of QMD detector and AIEZ framework.

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