A geomagnetic field based positioning technique for underground mines

Haverinen, Janne; Kemppainen, Anssi

doi:10.1109/rose.2011.6058533

Cited by 21 publications

(17 citation statements)

References 10 publications

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“…Haverinen et al [4] have conducted experiments in underground mines with high magnetic field using wheel encoded odometry and magnetic maps. The results show a better mean accuracy of 1.2 -1.44 m.…”

Section: Ambient Magnetic Field Based Localizationmentioning

confidence: 99%

“…We show that the use of magnetic maps (magnitude or vector), inertial sensors and indoor maps together improves the accuracy of [ocalization. [n earlier studies either magnetic maps [1,2,4,6] or indoor maps [7,8,11,13] or WiFi maps [7,11,[3,[7] have been used along with inertial sensors for localization.…”

Section: Introductionmentioning

confidence: 99%

“…Instead of using wheel encoders [4] or foot mounted inertial sensors [6] for direction estimation, the proposed method uses just a smartphone and reduces orientation errors due to indoor magnetic perturbations, gyro-drift using AHRS algorithm [10,15]. This makes our approach more general purpose for real life applications.…”

mentioning

confidence: 99%

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Smartphone based indoor tracking using magnetic and indoor maps

Putta

Misra

Kapoor

2015

2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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Tracking target user's indoor location with sub meter accuracy using low cost or no infrastructure is an active research topic. Indoor localization uses short range signals like WiFi, radio, ultrasound or Bluetooth signals that are affected by multipath errors and human presence. Recent studies have used stable magnetic field maps along with inertial sensors for indoor localization. In this paper, we propose a method that uses inertial sensors, magnetic field maps and indoor maps in a particle filter based implementation to improve accuracy of localization and tracking. Our method uses gradient descent algorithm to correct inaccurate user heading direction estimates due to magnetic perturbations. The tracking performance of our method is tested for four different implementations of Particle filter algorithm using magnetic maps (magnitude or vector) with and without indoor maps. We have developed an Android application to implement these four approaches and performed several experiments in a test area. The best approach out of these four achieves a mean localization accuracy of 0.75 m with a standard deviation of 0.52 m.

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Section: Ambient Magnetic Field Based Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Smartphone based indoor tracking using magnetic and indoor maps

Putta

Misra

Kapoor

2015

2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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“…Magnetometers have been used in geophysical research [1], military defence, mineral resources, drilling and mining practice [2]. These sensors measure the strength and direction of the local magnetic field.…”

Section: Introductionmentioning

confidence: 99%

Calibration of low-cost triaxial magnetometer

2016

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Abstract. Heading angle of magnetic compasses is estimated using the triaxial magnetometer readings. The accuracy of these readings is influenced by many factors such as sensor errors (scale factors, non-orthogonality, and offsets), and magnetic deviations (soft-iron and hard-iron interference). To calculate the true heading angle, the magnetic calibration of magnetometer is necessary. This research paper describes a calibration procedure for triaxial low-cost MEMS magnetometer. The proposed calibration method determines twelve calibration parameters in 3D arbitrary rotations of magnetometer.

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“…Recently, many works have demonstrated the feasibility of applying indoor magnetic measurements to localization problems both for pedestrians [8][9][10][11][12][13] and mobile robots [14][15][16]. Since the magnetic field in indoor environments is stable in the temporal domain and sufficiently varying in the spatial domain [17,18], we can also exploit these characteristics to generate constraints of the pose graphs.…”

Section: Introductionmentioning

confidence: 99%